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| author | Jan Aalmoes <jan.aalmoes@inria.fr> | 2024-09-13 00:07:42 +0200 |
|---|---|---|
| committer | Jan Aalmoes <jan.aalmoes@inria.fr> | 2024-09-13 00:07:42 +0200 |
| commit | faa07a8f3337c5d191597ea9b9587cc0969d663c (patch) | |
| tree | a46440db847ce447917abecb7971d90db4a1f150 | |
| parent | 7fc151d6a198d13dc9e1374522ec396d72905d3f (diff) | |
avnacé aia, remerciement notations, notes
170 files changed, 4587 insertions, 28 deletions
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b/ACSAC/figures/egd/meps/old_format/meps_egd_utility.pdf diff --git a/ACSAC/figures/fig_advdebias_attacc.tex b/ACSAC/figures/fig_advdebias_attacc.tex new file mode 100644 index 0000000..6c4d29a --- /dev/null +++ b/ACSAC/figures/fig_advdebias_attacc.tex @@ -0,0 +1,139 @@ +% \begin{figure}[!htb] +% \centering +% \begin{minipage}[b]{\linewidth} +% \centering +% \subfigure[\census (\race)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_soft_experimental_race.pdf} +% }% +% \subfigure[\census (\sex)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_soft_experimental_sex.pdf} +% } +% \end{minipage}% + + +% \begin{minipage}[b]{\linewidth} +% \centering +% \subfigure[\compas (\race)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_race.pdf} +% }% +% \subfigure[\compas (\sex)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_sex.pdf} +% } +% \end{minipage}% + +% \begin{minipage}[b]{\linewidth} +% \centering +% \subfigure[\meps (\race)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_soft_experimental_race.pdf} +% }% +% \subfigure[\meps (\sex)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_soft_experimental_sex.pdf} +% } +% \end{minipage}% + +% \begin{minipage}[b]{\linewidth} +% \centering +% \subfigure[\lfw (\race)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_race.pdf} +% }% +% \subfigure[\lfw (\sex)]{ +% \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_sex.pdf} +% } +% \end{minipage}% + +% \caption{%For both \adaptiveAIASoft and \adaptiveAIAHard, w +% We observe that \advdebias reduces the attack accuracy to random guess ($\sim$50\%). %Additionally for \adaptiveAIAHard, the theoretical bound on attack accuracy (``Theory'') matches with the empirical results (``Empirical''). +% } +% \label{fig:AdaptAIADebias} +% \end{figure} + + + + + +\begin{figure*}[!htb] + \centering + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIASoft: \census (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_soft_experimental_race.pdf} + }% + \subfigure[\adaptiveAIASoft: \census (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_soft_experimental_sex.pdf} + } + \end{minipage}% + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIAHard: \census (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_hard_race.pdf} + }% + \subfigure[\adaptiveAIAHard: \census (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_hard_sex.pdf} + } + \end{minipage}\\ + + + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIASoft: \compas (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_race.pdf} + }% + \subfigure[\adaptiveAIASoft: \compas (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_sex.pdf} + } + \end{minipage}% + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIAHard: \compas (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_hard_race.pdf} + }% + \subfigure[\adaptiveAIAHard: \compas (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_hard_sex.pdf} + } + \end{minipage}\\ + + + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIASoft: \meps (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_soft_experimental_race.pdf} + }% + \subfigure[\adaptiveAIASoft: \meps (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_soft_experimental_sex.pdf} + } + \end{minipage}% + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIAHard: \meps (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_hard_race.pdf} + }% + \subfigure[\adaptiveAIAHard: \meps (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_hard_sex.pdf} + } + \end{minipage}\\ + + + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIASoft: \lfw (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_race.pdf} + }% + \subfigure[\adaptiveAIASoft: \lfw (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_sex.pdf} + } + \end{minipage}% + \begin{minipage}[b]{0.49\linewidth} + \centering + \subfigure[\adaptiveAIAHard: \lfw (\race)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_hard_race.pdf} + }% + \subfigure[\adaptiveAIAHard: \lfw (\sex)]{ + \includegraphics[width=0.48\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_hard_sex.pdf} + } + \end{minipage} + +\vspace{-2mm} + \caption{For both \adaptiveAIASoft and \adaptiveAIAHard, \advdebias reduces the attack accuracy to random guess ($\sim$50\%). For \adaptiveAIAHard, the theoretical bound on attack accuracy (\theoretical) matches with the empirical results (\empirical).} + \label{fig:AdaptAIADebias} + \vspace{-2mm} +\end{figure*} diff --git a/ACSAC/figures/fig_advdebias_fair.tex b/ACSAC/figures/fig_advdebias_fair.tex new file mode 100644 index 0000000..42e6835 --- /dev/null +++ b/ACSAC/figures/fig_advdebias_fair.tex @@ -0,0 +1,47 @@ + +\begin{figure}[!htb] + \centering + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\census (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_dp_lvl_race.pdf} + }% + \subfigure[\census (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_dp_lvl_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\compas (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_dp_lvl_race.pdf} + }% + \subfigure[\compas (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_dp_lvl_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\meps (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_dp_lvl_race.pdf} + }% + \subfigure[\meps (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_dp_lvl_sex.pdf} + } + \end{minipage}\\ + + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\lfw (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_dp_lvl_race.pdf} + }% + \subfigure[\lfw (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_dp_lvl_sex.pdf} + } + \end{minipage} + + \caption{\dempar-Level for \advdebias: We observe that \dempar-Level is lower for \advdebias indicating $\targetmodel$ is fair.} + \label{fig:DemParAdvDebias2} +\end{figure}
\ No newline at end of file diff --git a/ACSAC/figures/fig_advdebias_utility.tex b/ACSAC/figures/fig_advdebias_utility.tex new file mode 100644 index 0000000..0a87115 --- /dev/null +++ b/ACSAC/figures/fig_advdebias_utility.tex @@ -0,0 +1,23 @@ +\begin{figure}[!htb] + \centering + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\census]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_utility.pdf} + }% + \subfigure[\compas]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_utility.pdf} + } + \end{minipage}\\ + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\meps]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_utility.pdf} + }% + \subfigure[\lfw]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_utility.pdf} + } + \end{minipage} + \caption{Utility degradation for \advdebias: We observe a statistically significant drop in $\targetmodel$'s accuracy on using \advdebias.} + \label{fig:utilityAdvDebias2} +\end{figure}
\ No newline at end of file diff --git a/ACSAC/figures/fig_egd_attack.tex b/ACSAC/figures/fig_egd_attack.tex new file mode 100644 index 0000000..8b1c713 --- /dev/null +++ b/ACSAC/figures/fig_egd_attack.tex @@ -0,0 +1,50 @@ + +\begin{figure}[!htb] + \centering + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\census (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/census/census_egd_attack_hard_race.pdf} + }% + \subfigure[\census (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/census/census_egd_attack_hard_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\compas (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/compas/compas_egd_attack_hard_race.pdf} + }% + \subfigure[\compas (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/compas/compas_egd_attack_hard_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\meps (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/meps/meps_egd_attack_hard_race.pdf} + }% + \subfigure[\meps (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/meps/meps_egd_attack_hard_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\lfw (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_attack_hard_race.pdf} + }% + \subfigure[\lfw (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_attack_hard_sex.pdf} + } + \end{minipage} + + \caption{For \adaptiveAIAHard, we observe that \egd reduces the attack accuracy to random guess ($\sim$50\%). %Additionally, the theoretical bound on attack accuracy (``Theory'') matches with the empirical results (``Empirical''). + } + \label{fig:AdaptAIAEGD} +\end{figure} + + +%\textbf{Overall, we observe that using group fairness results in attribute privacy but comes at the cost of $\targetmodel$'s utility.}
\ No newline at end of file diff --git a/ACSAC/figures/fig_egd_fair.tex b/ACSAC/figures/fig_egd_fair.tex new file mode 100644 index 0000000..bed88b8 --- /dev/null +++ b/ACSAC/figures/fig_egd_fair.tex @@ -0,0 +1,46 @@ + +\begin{figure}[!htb] + \centering + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\census (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/census/census_egd_dp_lvl_race.pdf} + }% + \subfigure[\census (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/census/census_egd_dp_lvl_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\compas (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/compas/compas_egd_dp_lvl_race.pdf} + }% + \subfigure[\compas (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/compas/compas_egd_dp_lvl_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\meps (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/meps/meps_egd_dp_lvl_race.pdf} + }% + \subfigure[\meps (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/meps/meps_egd_dp_lvl_sex.pdf} + } + \end{minipage}\\ + + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\lfw (\race)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_dp_lvl_race.pdf} + }% + \subfigure[\lfw (\sex)]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_dp_lvl_sex.pdf} + } + \end{minipage} + + \caption{\dempar-Level for \egd: We observe that \dempar-Level is lower for \egd than the baseline indicating $\targetmodel$ is fair after \egd.} + \label{fig:DemParegd2} +\end{figure}
\ No newline at end of file diff --git a/ACSAC/figures/fig_egd_utility.tex b/ACSAC/figures/fig_egd_utility.tex new file mode 100644 index 0000000..36ae32a --- /dev/null +++ b/ACSAC/figures/fig_egd_utility.tex @@ -0,0 +1,20 @@ +\begin{figure}[!htb] + \centering + \begin{minipage}[b]{1\linewidth} + \centering + \subfigure[\census]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/census/census_egd_utility.pdf} + }% + \subfigure[\compas]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/compas/compas_egd_utility.pdf} + }\\ + \subfigure[\meps]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/meps/meps_egd_utility.pdf} + }% + \subfigure[\lfw]{ + \includegraphics[width=0.49\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_utility.pdf} + } + \end{minipage} + \caption{Utility degradation for \egd: We observe a statistically significant drop in $\targetmodel$'s accuracy on using \egddp which matches the observation from prior work~\cite{reductions}.} + \label{fig:utilityEGD} +\end{figure}
\ No newline at end of file diff --git a/ACSAC/figures/fig_tm2.tex b/ACSAC/figures/fig_tm2.tex new file mode 100644 index 0000000..9cf489b --- /dev/null +++ b/ACSAC/figures/fig_tm2.tex @@ -0,0 +1,47 @@ +\begin{figure}[t] +\centering +\resizebox{.68\textwidth}{!}{% +\begin{tikzpicture} + + \node [rectangle,draw,thick,minimum width=1.5cm, minimum height=0.75cm] (targetmodel) {$\targetmodel$}; + + \node[below of=targetmodel,yshift=-0.3cm,database,database radius=0.4cm,database segment height=0.2cm, label={below:\footnotesize $\traindata: (X, Y)$}] (trainingdata) {}; + + \node [left of=targetmodel,minimum width=0.75cm,xshift=-0.8cm,fill=red!20,rectangle,draw,thick,label={below:\footnotesize Input}] (inputrecord) {$X'(\omega)$}; + \node [right of=targetmodel,xshift=1.5cm,rectangle,draw,thick] (outputpred) {$\targetmodel(X'(\omega))$}; + % \node [below of=outputpred,minimum width=1.5cm,rectangle,draw,thick] (explanation) {$\phi(x)$}; + + \begin{scope}[on background layer] + \node (tm1) [fit=(targetmodel) (trainingdata), fill= gray!20, rounded corners, inner sep=0.1cm, label={above:\footnotesize }] {}; + \end{scope} + + \node [right of=outputpred,rectangle,draw,thick,minimum width=1.5cm, minimum height=0.75cm,xshift=1.2cm,fill= gray!20] (attmodel) {$\attackmodel$}; + + \node [right of=attmodel,xshift=0.6cm,minimum width=0.75cm,rectangle,draw,thick] (attout) {$S(\omega)$}; + + \node[below of=attmodel,database,database radius=0.4cm,database segment height=0.2cm,yshift=-0.3cm, label={below:\footnotesize $\auxdata: (X', Y', S')$}] (auxdata) {}; + +\begin{scope}[on background layer] + \node (models) [fit=(attmodel) (outputpred) (attout) (auxdata), fill= red!20, rounded corners, inner sep=0.1cm] {}; +\end{scope} + + +\draw[->,ultra thick] (inputrecord.east) -- node[anchor=south, align=center] {\em\footnotesize } (targetmodel.west); +\draw[->,ultra thick] (targetmodel.east) -- node[anchor=south, align=center] {\em\footnotesize } (outputpred.west); +% \draw[->,ultra thick] (targetmodel.east) -- node[anchor=south, align=center] {\em\footnotesize } (explanation.west); + + +\draw[->,ultra thick, dashed] (outputpred.east) -- node[anchor=south, align=center] {\em\footnotesize } (attmodel.west); +% \draw[->,ultra thick, dashed] (explanation.east) -- node[anchor=south, align=center] {\em\footnotesize } (attmodel.west); +\draw[->,ultra thick] (attmodel.east) -- node[anchor=south, align=center] {\em\footnotesize } (attout.west); +\draw[->,ultra thick,dashed] (trainingdata.north) -- node[anchor=south, align=center,label={[yshift=-0.2cm]right:\footnotesize Train}] {\em\footnotesize } (targetmodel.south); +\draw[->,ultra thick,dashed] (auxdata.north) -- node[anchor=south, align=center,label={[yshift=-0.2cm]right:\footnotesize Train}] {\em\footnotesize } (attmodel.south); + + +\end{tikzpicture} +} +\vspace{-1mm} +\caption{\adv wants to infer sensitive attributes for an input given its prediction. \adv trains $\attackmodel$ on $\auxdata$ to map $\targetmodel(X'(\omega))$ to $S'(\omega)$. Once trained, \adv only uses $\targetmodel$'s outputs as input to $\attackmodel$ to infer sensitive attributes. \colorbox{red!20}{red} indicates accessible by \adv.} +\vspace{-1mm} +\label{fig:tm2} +\end{figure}
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version 2.24 (29-Jan-2024) +% Springer Verlag LaTeX2e support for Lecture Notes in Computer Science +% +%% +%% \CharacterTable +%% {Upper-case \A\B\C\D\E\F\G\H\I\J\K\L\M\N\O\P\Q\R\S\T\U\V\W\X\Y\Z +%% Lower-case \a\b\c\d\e\f\g\h\i\j\k\l\m\n\o\p\q\r\s\t\u\v\w\x\y\z +%% Digits \0\1\2\3\4\5\6\7\8\9 +%% Exclamation \! Double quote \" Hash (number) \# +%% Dollar \$ Percent \% Ampersand \& +%% Acute accent \' Left paren \( Right paren \) +%% Asterisk \* Plus \+ Comma \, +%% Minus \- Point \. Solidus \/ +%% Colon \: Semicolon \; Less than \< +%% Equals \= Greater than \> Question mark \? +%% Commercial at \@ Left bracket \[ Backslash \\ +%% Right bracket \] Circumflex \^ Underscore \_ +%% Grave accent \` Left brace \{ Vertical bar \| +%% Right brace \} Tilde \~} +%% +\NeedsTeXFormat{LaTeX2e}[1995/12/01] +\ProvidesClass{llncs}[2024/01/29 v2.24 +^^J LaTeX document class for Lecture Notes in Computer Science] +% Options +\let\if@envcntreset\iffalse +\DeclareOption{envcountreset}{\let\if@envcntreset\iftrue} +\DeclareOption{citeauthoryear}{\let\citeauthoryear=Y} +\DeclareOption{oribibl}{\let\oribibl=Y} +\let\if@custvec\iftrue +\DeclareOption{orivec}{\let\if@custvec\iffalse} +\let\if@envcntsame\iffalse +\DeclareOption{envcountsame}{\let\if@envcntsame\iftrue} +\let\if@envcntsect\iffalse +\DeclareOption{envcountsect}{\let\if@envcntsect\iftrue} +\let\if@runhead\iffalse +\DeclareOption{runningheads}{\let\if@runhead\iftrue} + +\let\if@openright\iftrue +\let\if@openbib\iffalse +\DeclareOption{openbib}{\let\if@openbib\iftrue} + +% languages +\let\switcht@@therlang\relax +\def\ds@deutsch{\def\switcht@@therlang{\switcht@deutsch}} +\def\ds@francais{\def\switcht@@therlang{\switcht@francais}} + +\DeclareOption*{\PassOptionsToClass{\CurrentOption}{article}} + +\ProcessOptions + +\LoadClass[twoside]{article} +\RequirePackage{multicol} % needed for the list of participants, index +\RequirePackage{aliascnt} + +\setlength{\textwidth}{12.2cm} +\setlength{\textheight}{19.3cm} +\renewcommand\@pnumwidth{2em} +\renewcommand\@tocrmarg{3.5em} +% +\def\@dottedtocline#1#2#3#4#5{% + \ifnum #1>\c@tocdepth \else + \vskip \z@ \@plus.2\p@ + {\leftskip #2\relax \rightskip \@tocrmarg \advance\rightskip by 0pt plus 2cm + \parfillskip -\rightskip \pretolerance=10000 + \parindent #2\relax\@afterindenttrue + \interlinepenalty\@M + \leavevmode + \@tempdima #3\relax + \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip + {#4}\nobreak + \leaders\hbox{$\m@th + \mkern \@dotsep mu\hbox{.}\mkern \@dotsep + mu$}\hfill + \nobreak + \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}% + \par}% + \fi} +% +\def\switcht@albion{% +\def\abstractname{Abstract.} +\def\ackname{Acknowledgments.} +\def\andname{and} +\def\lastandname{\unskip, and} +\def\appendixname{Appendix} +\def\chaptername{Chapter} +\def\claimname{Claim} +\def\conjecturename{Conjecture} +\def\contentsname{Table of Contents} +\def\corollaryname{Corollary} +\def\definitionname{Definition} +\def\discintname{Disclosure of Interests.} +\def\examplename{Example} +\def\exercisename{Exercise} +\def\figurename{Fig.} +\def\keywordname{{\bf Keywords:}} +\def\indexname{Index} +\def\lemmaname{Lemma} +\def\contriblistname{List of Contributors} +\def\listfigurename{List of Figures} +\def\listtablename{List of Tables} +\def\mailname{{\it Correspondence to\/}:} +\def\noteaddname{Note added in proof} +\def\notename{Note} +\def\partname{Part} +\def\problemname{Problem} +\def\proofname{Proof} +\def\propertyname{Property} +\def\propositionname{Proposition} +\def\questionname{Question} +\def\remarkname{Remark} +\def\seename{see} +\def\solutionname{Solution} +\def\subclassname{{\it Subject Classifications\/}:} +\def\tablename{Table} +\def\theoremname{Theorem}} +\switcht@albion +% Names of theorem like environments are already defined +% but must be translated if another language is chosen +% +% French section +\def\switcht@francais{%\typeout{On parle francais.}% + \def\abstractname{R\'esum\'e.}% + \def\ackname{Remerciements.}% + \def\andname{et}% + \def\lastandname{ et}% + \def\appendixname{Appendice}% + \def\chaptername{Chapitre}% + \def\claimname{Pr\'etention}% + \def\conjecturename{Hypoth\`ese}% + \def\contentsname{Table des mati\`eres}% + \def\corollaryname{Corollaire}% + \def\definitionname{D\'efinition}% + \def\discintname{Mention des Int\'{e}r\^{e}ts.} + \def\examplename{Exemple}% + \def\exercisename{Exercice}% + \def\figurename{Fig.}% + \def\keywordname{{\bf Mots-cl\'e:}}% + \def\indexname{Index}% + \def\lemmaname{Lemme}% + \def\contriblistname{Liste des contributeurs}% + \def\listfigurename{Liste des figures}% + \def\listtablename{Liste des tables}% + \def\mailname{{\it Correspondence to\/}:}% + \def\noteaddname{Note ajout\'ee \`a l'\'epreuve}% + \def\notename{Remarque}% + \def\partname{Partie}% + \def\problemname{Probl\`eme}% + \def\proofname{Preuve}% + \def\propertyname{Caract\'eristique}% +%\def\propositionname{Proposition}% + \def\questionname{Question}% + \def\remarkname{Remarque}% + \def\seename{voir}% + \def\solutionname{Solution}% + \def\subclassname{{\it Subject Classifications\/}:}% + \def\tablename{Tableau}% + \def\theoremname{Th\'eor\`eme}% +} +% +% German section +\def\switcht@deutsch{%\typeout{Man spricht deutsch.}% + \def\abstractname{Zusammenfassung.}% + \def\ackname{Danksagung.}% + \def\andname{und}% + \def\lastandname{ und}% + \def\appendixname{Anhang}% + \def\chaptername{Kapitel}% + \def\claimname{Behauptung}% + \def\conjecturename{Hypothese}% + \def\contentsname{Inhaltsverzeichnis}% + \def\corollaryname{Korollar}% +%\def\definitionname{Definition}% + \def\discintname{Offenlegung von Interessen.} + \def\examplename{Beispiel}% + \def\exercisename{\"Ubung}% + \def\figurename{Abb.}% + \def\keywordname{{\bf Schl\"usselw\"orter:}}% + \def\indexname{Index}% +%\def\lemmaname{Lemma}% + \def\contriblistname{Mitarbeiter}% + \def\listfigurename{Abbildungsverzeichnis}% + \def\listtablename{Tabellenverzeichnis}% + \def\mailname{{\it Correspondence to\/}:}% + \def\noteaddname{Nachtrag}% + \def\notename{Anmerkung}% + \def\partname{Teil}% +%\def\problemname{Problem}% + \def\proofname{Beweis}% + \def\propertyname{Eigenschaft}% +%\def\propositionname{Proposition}% + \def\questionname{Frage}% + \def\remarkname{Anmerkung}% + \def\seename{siehe}% + \def\solutionname{L\"osung}% + \def\subclassname{{\it Subject Classifications\/}:}% + \def\tablename{Tabelle}% +%\def\theoremname{Theorem}% +} + +% Ragged bottom for the actual page +\def\thisbottomragged{\def\@textbottom{\vskip\z@ plus.0001fil +\global\let\@textbottom\relax}} + +\renewcommand\small{% + \@setfontsize\small\@ixpt{11}% + \abovedisplayskip 8.5\p@ \@plus3\p@ \@minus4\p@ + \abovedisplayshortskip \z@ \@plus2\p@ + \belowdisplayshortskip 4\p@ \@plus2\p@ \@minus2\p@ + \def\@listi{\leftmargin\leftmargini + \parsep 0\p@ \@plus1\p@ \@minus\p@ + \topsep 8\p@ \@plus2\p@ \@minus4\p@ + \itemsep0\p@}% + \belowdisplayskip \abovedisplayskip +} + +% Switch to small font size for the credits at the end of the paper +% (i.e. Acknowlegments and Disclosure of Interests) +\newenvironment{credits}{% +\begingroup\small% +\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}% + {-12\p@ \@plus -4\p@ \@minus -4\p@}% + {-0.5em \@plus -0.22em \@minus -0.1em}% + {\normalfont\small\bfseries\boldmath}} +\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}% + {-8\p@ \@plus -4\p@ \@minus -4\p@}% + {-0.5em \@plus -0.22em \@minus -0.1em}% + {\normalfont\small\itshape}}% +}{\endgroup} + +\frenchspacing +\widowpenalty=10000 +\clubpenalty=10000 + +\setlength\oddsidemargin {63\p@} +\setlength\evensidemargin {63\p@} +\setlength\marginparwidth {90\p@} + +\setlength\headsep {16\p@} + +\setlength\footnotesep{7.7\p@} +\setlength\textfloatsep{8mm\@plus 2\p@ \@minus 4\p@} +\setlength\intextsep {8mm\@plus 2\p@ \@minus 2\p@} + +\setcounter{secnumdepth}{2} + +\newcounter {chapter} +\renewcommand\thechapter {\@arabic\c@chapter} + +\newif\if@mainmatter \@mainmattertrue +\newcommand\frontmatter{\cleardoublepage + \@mainmatterfalse\pagenumbering{Roman}} +\newcommand\mainmatter{\cleardoublepage + \@mainmattertrue\pagenumbering{arabic}} +\newcommand\backmatter{\if@openright\cleardoublepage\else\clearpage\fi + \@mainmatterfalse} + +\renewcommand\part{\cleardoublepage + \thispagestyle{empty}% + \if@twocolumn + \onecolumn + \@tempswatrue + \else + \@tempswafalse + \fi + \null\vfil + \secdef\@part\@spart} + +\def\@part[#1]#2{% + \ifnum \c@secnumdepth >-2\relax + \refstepcounter{part}% + \addcontentsline{toc}{part}{\thepart\hspace{1em}#1}% + \else + \addcontentsline{toc}{part}{#1}% + \fi + \markboth{}{}% + {\centering + \interlinepenalty \@M + \normalfont + \ifnum \c@secnumdepth >-2\relax + \huge\bfseries \partname~\thepart + \par + \vskip 20\p@ + \fi + \Huge \bfseries #2\par}% + \@endpart} +\def\@spart#1{% + {\centering + \interlinepenalty \@M + \normalfont + \Huge \bfseries #1\par}% + \@endpart} +\def\@endpart{\vfil\newpage + \if@twoside + \null + \thispagestyle{empty}% + \newpage + \fi + \if@tempswa + \twocolumn + \fi} + +\newcommand\chapter{\clearpage + \thispagestyle{empty}% + \global\@topnum\z@ + \@afterindentfalse + \secdef\@chapter\@schapter} +\def\@chapter[#1]#2{\ifnum \c@secnumdepth >\m@ne + \if@mainmatter + \refstepcounter{chapter}% + \typeout{\@chapapp\space\thechapter.}% + \addcontentsline{toc}{chapter}% + {\protect\numberline{\thechapter}#1}% + \else + \addcontentsline{toc}{chapter}{#1}% + \fi + \else + \addcontentsline{toc}{chapter}{#1}% + \fi + \chaptermark{#1}% + \addtocontents{lof}{\protect\addvspace{10\p@}}% + \addtocontents{lot}{\protect\addvspace{10\p@}}% + \if@twocolumn + \@topnewpage[\@makechapterhead{#2}]% + \else + \@makechapterhead{#2}% + \@afterheading + \fi} +\def\@makechapterhead#1{% +% \vspace*{50\p@}% + {\centering + \ifnum \c@secnumdepth >\m@ne + \if@mainmatter + \large\bfseries \@chapapp{} \thechapter + \par\nobreak + \vskip 20\p@ + \fi + \fi + \interlinepenalty\@M + \Large \bfseries #1\par\nobreak + \vskip 40\p@ + }} +\def\@schapter#1{\if@twocolumn + \@topnewpage[\@makeschapterhead{#1}]% + \else + \@makeschapterhead{#1}% + \@afterheading + \fi} +\def\@makeschapterhead#1{% +% \vspace*{50\p@}% + {\centering + \normalfont + \interlinepenalty\@M + \Large \bfseries #1\par\nobreak + \vskip 40\p@ + }} + +\renewcommand\section{\@startsection{section}{1}{\z@}% + {-18\p@ \@plus -4\p@ \@minus -4\p@}% + {12\p@ \@plus 4\p@ \@minus 4\p@}% + {\normalfont\large\bfseries\boldmath + \rightskip=\z@ \@plus 8em\pretolerance=10000 }} +\renewcommand\subsection{\@startsection{subsection}{2}{\z@}% + {-18\p@ \@plus -4\p@ \@minus -4\p@}% + {8\p@ \@plus 4\p@ \@minus 4\p@}% + {\normalfont\normalsize\bfseries\boldmath + \rightskip=\z@ \@plus 8em\pretolerance=10000 }} +\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}% + {-18\p@ \@plus -4\p@ \@minus -4\p@}% + {-0.5em \@plus -0.22em \@minus -0.1em}% + {\normalfont\normalsize\bfseries\boldmath}} +\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}% + {-12\p@ \@plus -4\p@ \@minus -4\p@}% + {-0.5em \@plus -0.22em \@minus -0.1em}% + {\normalfont\normalsize\itshape}} +\renewcommand\subparagraph[1]{\typeout{LLNCS warning: You should not use + \string\subparagraph\space with this class}\vskip0.5cm +You should not use \verb|\subparagraph| with this class.\vskip0.5cm} + +\DeclareMathSymbol{\Gamma}{\mathalpha}{letters}{"00} +\DeclareMathSymbol{\Delta}{\mathalpha}{letters}{"01} +\DeclareMathSymbol{\Theta}{\mathalpha}{letters}{"02} +\DeclareMathSymbol{\Lambda}{\mathalpha}{letters}{"03} +\DeclareMathSymbol{\Xi}{\mathalpha}{letters}{"04} +\DeclareMathSymbol{\Pi}{\mathalpha}{letters}{"05} +\DeclareMathSymbol{\Sigma}{\mathalpha}{letters}{"06} +\DeclareMathSymbol{\Upsilon}{\mathalpha}{letters}{"07} +\DeclareMathSymbol{\Phi}{\mathalpha}{letters}{"08} +\DeclareMathSymbol{\Psi}{\mathalpha}{letters}{"09} +\DeclareMathSymbol{\Omega}{\mathalpha}{letters}{"0A} + +\let\footnotesize\small + +\if@custvec +\DeclareRobustCommand\vec[1]{\mathchoice{\mbox{\boldmath$\displaystyle#1$}} +{\mbox{\boldmath$\textstyle#1$}} +{\mbox{\boldmath$\scriptstyle#1$}} +{\mbox{\boldmath$\scriptscriptstyle#1$}}} +\fi + +\def\squareforqed{\hbox{\rlap{$\sqcap$}$\sqcup$}} +\def\qed{\ifmmode\squareforqed\else{\unskip\nobreak\hfil +\penalty50\hskip1em\null\nobreak\hfil\squareforqed +\parfillskip=0pt\finalhyphendemerits=0\endgraf}\fi} + +\def\getsto{\mathrel{\mathchoice {\vcenter{\offinterlineskip +\halign{\hfil +$\displaystyle##$\hfil\cr\gets\cr\to\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr\gets +\cr\to\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr\gets +\cr\to\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +\gets\cr\to\cr}}}}} +\def\lid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil +$\displaystyle##$\hfil\cr<\cr\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr<\cr +\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr<\cr +\noalign{\vskip1pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +<\cr +\noalign{\vskip0.9pt}=\cr}}}}} +\def\gid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil +$\displaystyle##$\hfil\cr>\cr\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr>\cr +\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr>\cr +\noalign{\vskip1pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +>\cr +\noalign{\vskip0.9pt}=\cr}}}}} +\def\grole{\mathrel{\mathchoice {\vcenter{\offinterlineskip +\halign{\hfil +$\displaystyle##$\hfil\cr>\cr\noalign{\vskip-1pt}<\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr +>\cr\noalign{\vskip-1pt}<\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr +>\cr\noalign{\vskip-0.8pt}<\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +>\cr\noalign{\vskip-0.3pt}<\cr}}}}} +\def\bbbr{{\rm I\!R}} %reelle Zahlen +\def\bbbm{{\rm I\!M}} +\def\bbbn{{\rm I\!N}} %natuerliche Zahlen +\def\bbbf{{\rm I\!F}} +\def\bbbh{{\rm I\!H}} +\def\bbbk{{\rm I\!K}} +\def\bbbp{{\rm I\!P}} +\def\bbbone{{\mathchoice {\rm 1\mskip-4mu l} {\rm 1\mskip-4mu l} +{\rm 1\mskip-4.5mu l} {\rm 1\mskip-5mu l}}} +\def\bbbc{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}}}} +\def\bbbq{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm +Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.8\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle\rm Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.8\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle\rm Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.7\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.7\ht0\hss}\box0}}}} +\def\bbbt{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm +T$}\hbox{\hbox to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle\rm T$}\hbox{\hbox +to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle\rm T$}\hbox{\hbox +to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm T$}\hbox{\hbox +to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}}}} +\def\bbbs{{\mathchoice +{\setbox0=\hbox{$\displaystyle \rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\hbox +to0pt{\kern0.55\wd0\vrule height0.5\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle \rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\hbox +to0pt{\kern0.55\wd0\vrule height0.5\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle \rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\raise0.05\ht0\hbox +to0pt{\kern0.5\wd0\vrule height0.45\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.4\wd0\vrule height0.45\ht0\hss}\raise0.05\ht0\hbox +to0pt{\kern0.55\wd0\vrule height0.45\ht0\hss}\box0}}}} +\def\bbbz{{\mathchoice {\hbox{$\mathsf\textstyle Z\kern-0.4em Z$}} +{\hbox{$\mathsf\textstyle Z\kern-0.4em Z$}} +{\hbox{$\mathsf\scriptstyle Z\kern-0.3em Z$}} +{\hbox{$\mathsf\scriptscriptstyle Z\kern-0.2em Z$}}}} + +\let\ts\, + +\setlength\leftmargini {17\p@} +\setlength\leftmargin {\leftmargini} +\setlength\leftmarginii {\leftmargini} +\setlength\leftmarginiii {\leftmargini} +\setlength\leftmarginiv {\leftmargini} +\setlength \labelsep {.5em} +\setlength \labelwidth{\leftmargini} +\addtolength\labelwidth{-\labelsep} + +\def\@listI{\leftmargin\leftmargini + \parsep 0\p@ \@plus1\p@ \@minus\p@ + \topsep 8\p@ \@plus2\p@ \@minus4\p@ + \itemsep0\p@} +\let\@listi\@listI +\@listi +\def\@listii {\leftmargin\leftmarginii + \labelwidth\leftmarginii + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus2\p@ \@minus\p@} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus\p@\@minus\p@ + \parsep \z@ + \partopsep \p@ \@plus\z@ \@minus\p@} + +\renewcommand\labelitemi{\normalfont\bfseries --} +\renewcommand\labelitemii{$\m@th\bullet$} + +\setlength\arraycolsep{1.4\p@} +\setlength\tabcolsep{1.4\p@} + +\def\tableofcontents{\chapter*{\contentsname\@mkboth{{\contentsname}}% + {{\contentsname}}} + \def\authcount##1{\setcounter{auco}{##1}\setcounter{@auth}{1}} + \def\lastand{\ifnum\value{auco}=2\relax + \unskip{} \andname\ + \else + \unskip \lastandname\ + \fi}% + \def\and{\stepcounter{@auth}\relax + \ifnum\value{@auth}=\value{auco}% + \lastand + \else + \unskip, + \fi}% + \@starttoc{toc}\if@restonecol\twocolumn\fi} + +\def\l@part#1#2{\addpenalty{\@secpenalty}% + \addvspace{2em plus\p@}% % space above part line + \begingroup + \parindent \z@ + \rightskip \z@ plus 5em + \hrule\vskip5pt + \large % same size as for a contribution heading + \bfseries\boldmath % set line in boldface + \leavevmode % TeX command to enter horizontal mode. + #1\par + \vskip5pt + \hrule + \vskip1pt + \nobreak % Never break after part entry + \endgroup} + +\def\@dotsep{2} + +\let\phantomsection=\relax + +\def\hyperhrefextend{\ifx\hyper@anchor\@undefined\else +{}\fi} + +\def\addnumcontentsmark#1#2#3{% +\addtocontents{#1}{\protect\contentsline{#2}{\protect\numberline + {\thechapter}#3}{\thepage}\hyperhrefextend}}% +\def\addcontentsmark#1#2#3{% +\addtocontents{#1}{\protect\contentsline{#2}{#3}{\thepage}\hyperhrefextend}}% +\def\addcontentsmarkwop#1#2#3{% +\addtocontents{#1}{\protect\contentsline{#2}{#3}{0}\hyperhrefextend}}% + +\def\@adcmk[#1]{\ifcase #1 \or +\def\@gtempa{\addnumcontentsmark}% + \or \def\@gtempa{\addcontentsmark}% + \or \def\@gtempa{\addcontentsmarkwop}% + \fi\@gtempa{toc}{chapter}% +} +\def\addtocmark{% +\phantomsection +\@ifnextchar[{\@adcmk}{\@adcmk[3]}% +} + +\def\l@chapter#1#2{\addpenalty{-\@highpenalty} + \vskip 1.0em plus 1pt \@tempdima 1.5em \begingroup + \parindent \z@ \rightskip \@tocrmarg + \advance\rightskip by 0pt plus 2cm + \parfillskip -\rightskip \pretolerance=10000 + \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip + {\large\bfseries\boldmath#1}\ifx0#2\hfil\null + \else + \nobreak + \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern + \@dotsep mu$}\hfill + \nobreak\hbox to\@pnumwidth{\hss #2}% + \fi\par + \penalty\@highpenalty \endgroup} + +\def\l@title#1#2{\addpenalty{-\@highpenalty} + \addvspace{8pt plus 1pt} + \@tempdima \z@ + \begingroup + \parindent \z@ \rightskip \@tocrmarg + \advance\rightskip by 0pt plus 2cm + \parfillskip -\rightskip \pretolerance=10000 + \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip + #1\nobreak + \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern + \@dotsep mu$}\hfill + \nobreak\hbox to\@pnumwidth{\hss #2}\par + \penalty\@highpenalty \endgroup} + +\def\l@author#1#2{\addpenalty{\@highpenalty} + \@tempdima=15\p@ %\z@ + \begingroup + \parindent \z@ \rightskip \@tocrmarg + \advance\rightskip by 0pt plus 2cm + \pretolerance=10000 + \leavevmode \advance\leftskip\@tempdima %\hskip -\leftskip + \textit{#1}\par + \penalty\@highpenalty \endgroup} + +\setcounter{tocdepth}{0} +\newdimen\tocchpnum +\newdimen\tocsecnum +\newdimen\tocsectotal +\newdimen\tocsubsecnum +\newdimen\tocsubsectotal +\newdimen\tocsubsubsecnum +\newdimen\tocsubsubsectotal +\newdimen\tocparanum +\newdimen\tocparatotal +\newdimen\tocsubparanum +\tocchpnum=\z@ % no chapter numbers +\tocsecnum=15\p@ % section 88. plus 2.222pt +\tocsubsecnum=23\p@ % subsection 88.8 plus 2.222pt +\tocsubsubsecnum=27\p@ % subsubsection 88.8.8 plus 1.444pt +\tocparanum=35\p@ % paragraph 88.8.8.8 plus 1.666pt +\tocsubparanum=43\p@ % subparagraph 88.8.8.8.8 plus 1.888pt +\def\calctocindent{% +\tocsectotal=\tocchpnum +\advance\tocsectotal by\tocsecnum +\tocsubsectotal=\tocsectotal +\advance\tocsubsectotal by\tocsubsecnum +\tocsubsubsectotal=\tocsubsectotal +\advance\tocsubsubsectotal by\tocsubsubsecnum +\tocparatotal=\tocsubsubsectotal +\advance\tocparatotal by\tocparanum} +\calctocindent + +\def\l@section{\@dottedtocline{1}{\tocchpnum}{\tocsecnum}} +\def\l@subsection{\@dottedtocline{2}{\tocsectotal}{\tocsubsecnum}} +\def\l@subsubsection{\@dottedtocline{3}{\tocsubsectotal}{\tocsubsubsecnum}} +\def\l@paragraph{\@dottedtocline{4}{\tocsubsubsectotal}{\tocparanum}} +\def\l@subparagraph{\@dottedtocline{5}{\tocparatotal}{\tocsubparanum}} + +\def\listoffigures{\@restonecolfalse\if@twocolumn\@restonecoltrue\onecolumn + \fi\section*{\listfigurename\@mkboth{{\listfigurename}}{{\listfigurename}}} + \@starttoc{lof}\if@restonecol\twocolumn\fi} +\def\l@figure{\@dottedtocline{1}{0em}{1.5em}} + +\def\listoftables{\@restonecolfalse\if@twocolumn\@restonecoltrue\onecolumn + \fi\section*{\listtablename\@mkboth{{\listtablename}}{{\listtablename}}} + \@starttoc{lot}\if@restonecol\twocolumn\fi} +\let\l@table\l@figure + +\renewcommand\listoffigures{% + \section*{\listfigurename + \@mkboth{\listfigurename}{\listfigurename}}% + \@starttoc{lof}% + } + +\renewcommand\listoftables{% + \section*{\listtablename + \@mkboth{\listtablename}{\listtablename}}% + \@starttoc{lot}% + } + +\ifx\oribibl\undefined +\ifx\citeauthoryear\undefined +\renewenvironment{thebibliography}[1] + {\section*{\refname} + \def\@biblabel##1{##1.} + \small + \list{\@biblabel{\@arabic\c@enumiv}}% + {\settowidth\labelwidth{\@biblabel{#1}}% + \leftmargin\labelwidth + \advance\leftmargin\labelsep + \if@openbib + \advance\leftmargin\bibindent + \itemindent -\bibindent + \listparindent \itemindent + \parsep \z@ + \fi + \usecounter{enumiv}% + \let\p@enumiv\@empty + \renewcommand\theenumiv{\@arabic\c@enumiv}}% + \if@openbib + \renewcommand\newblock{\par}% + \else + \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}% + \fi + \sloppy\clubpenalty4000\widowpenalty4000% + \sfcode`\.=\@m} + {\def\@noitemerr + {\@latex@warning{Empty `thebibliography' environment}}% + \endlist} +\def\@lbibitem[#1]#2{\item[{[#1]}\hfill]\if@filesw + {\let\protect\noexpand\immediate + \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces} +\newcount\@tempcntc +\def\@citex[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi + \@tempcnta\z@\@tempcntb\m@ne\def\@citea{}\@cite{\@for\@citeb:=#2\do + {\@ifundefined + {b@\@citeb}{\@citeo\@tempcntb\m@ne\@citea\def\@citea{,}{\bfseries + ?}\@warning + {Citation `\@citeb' on page \thepage \space undefined}}% + {\setbox\z@\hbox{\global\@tempcntc0\csname b@\@citeb\endcsname\relax}% + \ifnum\@tempcntc=\z@ \@citeo\@tempcntb\m@ne + \@citea\def\@citea{,}\hbox{\csname b@\@citeb\endcsname}% + \else + \advance\@tempcntb\@ne + \ifnum\@tempcntb=\@tempcntc + \else\advance\@tempcntb\m@ne\@citeo + \@tempcnta\@tempcntc\@tempcntb\@tempcntc\fi\fi}}\@citeo}{#1}} +\def\@citeo{\ifnum\@tempcnta>\@tempcntb\else + \@citea\def\@citea{,\,\hskip\z@skip}% + \ifnum\@tempcnta=\@tempcntb\the\@tempcnta\else + {\advance\@tempcnta\@ne\ifnum\@tempcnta=\@tempcntb \else + \def\@citea{--}\fi + \advance\@tempcnta\m@ne\the\@tempcnta\@citea\the\@tempcntb}\fi\fi} +\else +\renewenvironment{thebibliography}[1] + {\section*{\refname} + \small + \list{}% + {\settowidth\labelwidth{}% + \leftmargin\parindent + \itemindent=-\parindent + \labelsep=\z@ + \if@openbib + \advance\leftmargin\bibindent + \itemindent -\bibindent + \listparindent \itemindent + \parsep \z@ + \fi + \usecounter{enumiv}% + \let\p@enumiv\@empty + \renewcommand\theenumiv{}}% + \if@openbib + \renewcommand\newblock{\par}% + \else + \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}% + \fi + \sloppy\clubpenalty4000\widowpenalty4000% + \sfcode`\.=\@m} + {\def\@noitemerr + {\@latex@warning{Empty `thebibliography' environment}}% + \endlist} + \def\@cite#1{#1}% + \def\@lbibitem[#1]#2{\item[]\if@filesw + {\def\protect##1{\string ##1\space}\immediate + \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces} + \fi +\else +\@cons\@openbib@code{\noexpand\small} +\fi + +\def\idxquad{\hskip 10\p@}% space that divides entry from number + +\def\@idxitem{\par\hangindent 10\p@} + +\def\subitem{\par\setbox0=\hbox{--\enspace}% second order + \noindent\hangindent\wd0\box0}% index entry + +\def\subsubitem{\par\setbox0=\hbox{--\,--\enspace}% third + \noindent\hangindent\wd0\box0}% order index entry + +\def\indexspace{\par \vskip 10\p@ plus5\p@ minus3\p@\relax} + +\renewenvironment{theindex} + {\@mkboth{\indexname}{\indexname}% + \thispagestyle{empty}\parindent\z@ + \parskip\z@ \@plus .3\p@\relax + \let\item\par + \def\,{\relax\ifmmode\mskip\thinmuskip + \else\hskip0.2em\ignorespaces\fi}% + \normalfont\small + \begin{multicols}{2}[\@makeschapterhead{\indexname}]% + } + {\end{multicols}} + +\renewcommand\footnoterule{% + \kern-3\p@ + \hrule\@width 2truecm + \kern2.6\p@} + \newdimen\fnindent + \fnindent1em +\long\def\@makefntext#1{% + \parindent \fnindent% + \leftskip \fnindent% + \noindent + \llap{\hb@xt@1em{\hss\@makefnmark\ }}\ignorespaces#1} + +\long\def\@makecaption#1#2{% + \small + \vskip\abovecaptionskip + \sbox\@tempboxa{{\bfseries #1.} #2}% + \ifdim \wd\@tempboxa >\hsize + {\bfseries #1.} #2\par + \else + \global \@minipagefalse + \hb@xt@\hsize{\hfil\box\@tempboxa\hfil}% + \fi + \vskip\belowcaptionskip} + +\def\fps@figure{htbp} +\def\fnum@figure{\figurename\thinspace\thefigure} +\def \@floatboxreset {% + \reset@font + \small + \@setnobreak + \@setminipage +} +\def\fps@table{htbp} +\def\fnum@table{\tablename~\thetable} +\renewenvironment{table} + {\setlength\abovecaptionskip{0\p@}% + \setlength\belowcaptionskip{10\p@}% + \@float{table}} + {\end@float} +\renewenvironment{table*} + {\setlength\abovecaptionskip{0\p@}% + \setlength\belowcaptionskip{10\p@}% + \@dblfloat{table}} + {\end@dblfloat} + +\long\def\@caption#1[#2]#3{\par\addcontentsline{\csname + ext@#1\endcsname}{#1}{\protect\numberline{\csname + the#1\endcsname}{\ignorespaces #2}}\begingroup + \@parboxrestore + \@makecaption{\csname fnum@#1\endcsname}{\ignorespaces #3}\par + \endgroup} + +% LaTeX does not provide a command to enter the authors institute +% addresses. The \institute command is defined here. + +\newcounter{@inst} +\newcounter{@auth} +\newcounter{auco} +\newdimen\instindent +\newbox\authrun +\newtoks\authorrunning +\newtoks\tocauthor +\newbox\titrun +\newtoks\titlerunning +\newtoks\toctitle + +\def\clearheadinfo{\gdef\@author{No Author Given}% + \gdef\@title{No Title Given}% + \gdef\@subtitle{}% + \gdef\@institute{No Institute Given}% + \gdef\@thanks{}% + \global\titlerunning={}\global\authorrunning={}% + \global\toctitle={}\global\tocauthor={}} + +\def\institute#1{\gdef\@institute{#1}} + +\def\institutename{\par + \begingroup + \parskip=\z@ + \parindent=\z@ + \setcounter{@inst}{1}% + \def\and{\par\stepcounter{@inst}% + \noindent$^{\the@inst}$\enspace\ignorespaces}% + \setbox0=\vbox{\def\thanks##1{}\@institute}% + \ifnum\c@@inst=1\relax + \gdef\fnnstart{0}% + \else + \xdef\fnnstart{\c@@inst}% + \setcounter{@inst}{1}% + \noindent$^{\the@inst}$\enspace + \fi + \ignorespaces + \@institute\par + \endgroup} + +\def\@fnsymbol#1{\ensuremath{\ifcase#1\or\star\or{\star\star}\or + {\star\star\star}\or \dagger\or \ddagger\or + \mathchar "278\or \mathchar "27B\or \|\or **\or \dagger\dagger + \or \ddagger\ddagger \else\@ctrerr\fi}} + +\def\inst#1{\unskip$^{#1}$} +\def\orcidID#1{\unskip$^{[#1]}$} % added MR 2018-03-10 +\def\fnmsep{\unskip$^,$} +\def\email#1{{\tt#1}} + +\AtBeginDocument{\@ifundefined{url}{\def\url#1{#1}}{}% +\@ifpackageloaded{babel}{% +\@ifundefined{extrasenglish}{}{\addto\extrasenglish{\switcht@albion}}% +\@ifundefined{extrasfrenchb}{}{\addto\extrasfrenchb{\switcht@francais}}% +\@ifundefined{extrasgerman}{}{\addto\extrasgerman{\switcht@deutsch}}% +\@ifundefined{extrasngerman}{}{\addto\extrasngerman{\switcht@deutsch}}% +}{\switcht@@therlang}% +\providecommand{\keywords}[1]{\def\and{{\textperiodcentered} }% +\par\addvspace\baselineskip +\noindent\keywordname\enspace\ignorespaces#1}% +\@ifpackageloaded{hyperref}{% +\def\doi#1{\href{https://doi.org/\detokenize{#1}}{\url{https://doi.org/#1}}}}{ +\def\doi#1{https://doi.org/\detokenize{#1}}} +} +\def\homedir{\~{ }} + +\def\subtitle#1{\gdef\@subtitle{#1}} +\clearheadinfo +% +%%% to avoid hyperref warnings +\providecommand*{\toclevel@author}{999} +%%% to make title-entry parent of section-entries +\providecommand*{\toclevel@title}{0} +% +\renewcommand\maketitle{\newpage +\phantomsection + \refstepcounter{chapter}% + \stepcounter{section}% + \setcounter{section}{0}% + \setcounter{subsection}{0}% + \setcounter{figure}{0} + \setcounter{table}{0} + \setcounter{equation}{0} + \setcounter{footnote}{0}% + \begingroup + \parindent=\z@ + \renewcommand\thefootnote{\@fnsymbol\c@footnote}% + \if@twocolumn + \ifnum \col@number=\@ne + \@maketitle + \else + \twocolumn[\@maketitle]% + \fi + \else + \newpage + \global\@topnum\z@ % Prevents figures from going at top of page. + \@maketitle + \fi + \thispagestyle{empty}\@thanks +% + \def\\{\unskip\ \ignorespaces}\def\inst##1{\unskip{}}% + \def\thanks##1{\unskip{}}\def\fnmsep{\unskip}% + \instindent=\hsize + \advance\instindent by-\headlineindent + \if!\the\toctitle!\addcontentsline{toc}{title}{\@title}\else + \addcontentsline{toc}{title}{\the\toctitle}\fi + \if@runhead + \if!\the\titlerunning!\else + \edef\@title{\the\titlerunning}% + \fi + \global\setbox\titrun=\hbox{\small\rm\unboldmath\ignorespaces\@title}% + \ifdim\wd\titrun>\instindent + \typeout{Title too long for running head. Please supply}% + \typeout{a shorter form with \string\titlerunning\space prior to + \string\maketitle}% + \global\setbox\titrun=\hbox{\small\rm + Title Suppressed Due to Excessive Length}% + \fi + \xdef\@title{\copy\titrun}% + \fi +% + \if!\the\tocauthor!\relax + {\def\and{\noexpand\protect\noexpand\and}% + \def\inst##1{}% added MR 2017-09-20 to remove inst numbers from the TOC + \def\orcidID##1{}% added MR 2017-09-20 to remove ORCID ids from the TOC + \protected@xdef\toc@uthor{\@author}}% + \else + \def\\{\noexpand\protect\noexpand\newline}% + \protected@xdef\scratch{\the\tocauthor}% + \protected@xdef\toc@uthor{\scratch}% + \fi + \addtocontents{toc}{\noexpand\protect\noexpand\authcount{\the\c@auco}}% + \addcontentsline{toc}{author}{\toc@uthor}% + \if@runhead + \if!\the\authorrunning! + \value{@inst}=\value{@auth}% + \setcounter{@auth}{1}% + \else + \edef\@author{\the\authorrunning}% + \fi + \global\setbox\authrun=\hbox{\def\inst##1{}% added MR 2017-09-20 to remove inst numbers from the runninghead + \def\orcidID##1{}% added MR 2017-09-20 to remove ORCID ids from the runninghead + \small\unboldmath\@author\unskip}% + \ifdim\wd\authrun>\instindent + \typeout{Names of authors too long for running head. Please supply}% + \typeout{a shorter form with \string\authorrunning\space prior to + \string\maketitle}% + \global\setbox\authrun=\hbox{\small\rm + Authors Suppressed Due to Excessive Length}% + \fi + \xdef\@author{\copy\authrun}% + \markboth{\@author}{\@title}% + \fi + \endgroup + \setcounter{footnote}{\fnnstart}% + \clearheadinfo} +% +\def\@maketitle{\newpage + \markboth{}{}% + \def\lastand{\ifnum\value{@inst}=2\relax + \unskip{} \andname\ + \else + \unskip \lastandname\ + \fi}% + \def\and{\stepcounter{@auth}\relax + \ifnum\value{@auth}=\value{@inst}% + \lastand + \else + \unskip, + \fi}% + \begin{center}% + \let\newline\\ + {\Large \bfseries\boldmath + \pretolerance=10000 + \@title \par}\vskip .8cm +\if!\@subtitle!\else {\large \bfseries\boldmath + \vskip -.65cm + \pretolerance=10000 + \@subtitle \par}\vskip .8cm\fi + \setbox0=\vbox{\setcounter{@auth}{1}\def\and{\stepcounter{@auth}}% + \def\thanks##1{}\@author}% + \global\value{@inst}=\value{@auth}% + \global\value{auco}=\value{@auth}% + \setcounter{@auth}{1}% +{\lineskip .5em +\noindent\ignorespaces +\@author\vskip.35cm} + {\small\institutename} + \end{center}% + } + +% definition of the "\spnewtheorem" command. +% +% Usage: +% +% \spnewtheorem{env_nam}{caption}[within]{cap_font}{body_font} +% or \spnewtheorem{env_nam}[numbered_like]{caption}{cap_font}{body_font} +% or \spnewtheorem*{env_nam}{caption}{cap_font}{body_font} +% +% New is "cap_font" and "body_font". It stands for +% fontdefinition of the caption and the text itself. +% +% "\spnewtheorem*" gives a theorem without number. +% +% A defined spnewthoerem environment is used as described +% by Lamport. +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\edef\@thmcountersep{} +\edef\@thmcounterend{.} + +\def\spnewtheorem{\@ifstar{\@sthm}{\@Sthm}} + +% definition of \spnewtheorem with number + +\def\@spnthm#1#2{\@ifnextchar[{\@spxnthm{#1}{#2}}{\@spynthm{#1}{#2}}} +\def\@Sthm#1{\@ifnextchar[{\@spothm{#1}}{\@spnthm{#1}}} + +% theorem-like environment with standard counter +\def\@spynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname + {\@definecounter{#1}% + \expandafter\xdef\csname the#1\endcsname{\@thmcounter{#1}}% + \expandafter\xdef\csname #1name\endcsname{#2}% + \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#3}{#4}}% + \global\@namedef{end#1}{\@endtheorem}}} + +% theorem-like environment with section-wise counter (envcountsect) +\def\@spxnthm#1#2[#3]#4#5{\expandafter\@ifdefinable\csname #1\endcsname + {\@definecounter{#1}\@addtoreset{#1}{#3}% + \expandafter\xdef\csname the#1\endcsname{\expandafter\noexpand + \csname the#3\endcsname \noexpand\@thmcountersep \@thmcounter{#1}}% + \expandafter\xdef\csname #1name\endcsname{#2}% + \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}% + \global\@namedef{end#1}{\@endtheorem}}} + +% theorem-like environment with shared counter (envcountsame) +\def\@spothm#1[#2]#3#4#5{% + \@ifundefined{c@#2}{\@latexerr{No theorem environment `#2' defined}\@eha}% + {\expandafter\@ifdefinable\csname #1\endcsname + {\newaliascnt{#1}{#2}% + \expandafter\xdef\csname #1name\endcsname{#3}% + \if@envcntsect + % the following line, introduced in v2.24, fixes incorrect hypertexnames + % when envcountsect is used in combination with envcountsame + \@addtoreset{#1}{section} + \fi + \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}% + \global\@namedef{end#1}{\@endtheorem}}}} + + + +\def\@spthm#1#2#3#4{\topsep 7\p@ \@plus2\p@ \@minus4\p@ +\refstepcounter{#1}% +\@ifnextchar[{\@spythm{#1}{#2}{#3}{#4}}{\@spxthm{#1}{#2}{#3}{#4}}} + +\def\@spxthm#1#2#3#4{\@spbegintheorem{#2}{\csname the#1\endcsname}{#3}{#4}% + \ignorespaces} + +\def\@spythm#1#2#3#4[#5]{\@spopargbegintheorem{#2}{\csname + the#1\endcsname}{#5}{#3}{#4}\ignorespaces} + +\def\@spbegintheorem#1#2#3#4{\trivlist + \item[\hskip\labelsep{#3#1\ #2\@thmcounterend}]#4} + +\def\@spopargbegintheorem#1#2#3#4#5{\trivlist + \item[\hskip\labelsep{#4#1\ #2}]{#4(#3)\@thmcounterend\ }#5} + +% definition of \spnewtheorem* without number + +\def\@sthm#1#2{\@Ynthm{#1}{#2}} + +\def\@Ynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname + {\global\@namedef{#1}{\@Thm{\csname #1name\endcsname}{#3}{#4}}% + \expandafter\xdef\csname #1name\endcsname{#2}% + \global\@namedef{end#1}{\@endtheorem}}} + +\def\@Thm#1#2#3{\topsep 7\p@ \@plus2\p@ \@minus4\p@ +\@ifnextchar[{\@Ythm{#1}{#2}{#3}}{\@Xthm{#1}{#2}{#3}}} + +\def\@Xthm#1#2#3{\@Begintheorem{#1}{#2}{#3}\ignorespaces} + +\def\@Ythm#1#2#3[#4]{\@Opargbegintheorem{#1} + {#4}{#2}{#3}\ignorespaces} + +\def\@Begintheorem#1#2#3{#3\trivlist + \item[\hskip\labelsep{#2#1\@thmcounterend}]} + +\def\@Opargbegintheorem#1#2#3#4{#4\trivlist + \item[\hskip\labelsep{#3#1}]{#3(#2)\@thmcounterend\ }} + +\if@envcntsect + \def\@thmcountersep{.} + \spnewtheorem{theorem}{Theorem}[section]{\bfseries}{\itshape} +\else + \spnewtheorem{theorem}{Theorem}{\bfseries}{\itshape} + \if@envcntreset + \@addtoreset{theorem}{section} + \else + \@addtoreset{theorem}{chapter} + \fi +\fi + +%definition of various theorem environments +\spnewtheorem*{claim}{Claim}{\itshape}{\rmfamily} +\spnewtheorem*{proof}{Proof}{\itshape}{\rmfamily} +\if@envcntsame % alle Umgebungen wie Theorem. + \def\spn@wtheorem#1#2#3#4{\@spothm{#1}[theorem]{#2}{#3}{#4}} +\else % alle Umgebungen mit eigenem Zaehler + \if@envcntsect % mit section numeriert + \def\spn@wtheorem#1#2#3#4{\@spxnthm{#1}{#2}[section]{#3}{#4}} + \else % nicht mit section numeriert + \if@envcntreset + \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4} + \@addtoreset{#1}{section}} + \else + \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4} + \@addtoreset{#1}{chapter}}% + \fi + \fi +\fi +\spn@wtheorem{case}{Case}{\itshape}{\rmfamily} +\spn@wtheorem{conjecture}{Conjecture}{\itshape}{\rmfamily} +\spn@wtheorem{corollary}{Corollary}{\bfseries}{\itshape} +\spn@wtheorem{definition}{Definition}{\bfseries}{\itshape} +\spn@wtheorem{example}{Example}{\itshape}{\rmfamily} +\spn@wtheorem{exercise}{Exercise}{\itshape}{\rmfamily} +\spn@wtheorem{lemma}{Lemma}{\bfseries}{\itshape} +\spn@wtheorem{note}{Note}{\itshape}{\rmfamily} +\spn@wtheorem{problem}{Problem}{\itshape}{\rmfamily} +\spn@wtheorem{property}{Property}{\itshape}{\rmfamily} +\spn@wtheorem{proposition}{Proposition}{\bfseries}{\itshape} +\spn@wtheorem{question}{Question}{\itshape}{\rmfamily} +\spn@wtheorem{solution}{Solution}{\itshape}{\rmfamily} +\spn@wtheorem{remark}{Remark}{\itshape}{\rmfamily} + +\def\@takefromreset#1#2{% + \def\@tempa{#1}% + \let\@tempd\@elt + \def\@elt##1{% + \def\@tempb{##1}% + \ifx\@tempa\@tempb\else + \@addtoreset{##1}{#2}% + \fi}% + \expandafter\expandafter\let\expandafter\@tempc\csname cl@#2\endcsname + \expandafter\def\csname cl@#2\endcsname{}% + \@tempc + \let\@elt\@tempd} + +\def\theopargself{\def\@spopargbegintheorem##1##2##3##4##5{\trivlist + \item[\hskip\labelsep{##4##1\ ##2}]{##4##3\@thmcounterend\ }##5} + \def\@Opargbegintheorem##1##2##3##4{##4\trivlist + \item[\hskip\labelsep{##3##1}]{##3##2\@thmcounterend\ }} + } + +\renewenvironment{abstract}{% + \list{}{\advance\topsep by0.35cm\relax\small + \leftmargin=1cm + \labelwidth=\z@ + \listparindent=\z@ + \itemindent\listparindent + \rightmargin\leftmargin}\item[\hskip\labelsep + \bfseries\abstractname]} + {\endlist} + +\newdimen\headlineindent % dimension for space between +\headlineindent=1.166cm % number and text of headings. + +\def\ps@headings{\let\@mkboth\@gobbletwo + \let\@oddfoot\@empty\let\@evenfoot\@empty + \def\@evenhead{\normalfont\small\rlap{\thepage}\hspace{\headlineindent}% + \leftmark\hfil} + \def\@oddhead{\normalfont\small\hfil\rightmark\hspace{\headlineindent}% + \llap{\thepage}} + \def\chaptermark##1{}% + \def\sectionmark##1{}% + \def\subsectionmark##1{}} + +\def\ps@titlepage{\let\@mkboth\@gobbletwo + \let\@oddfoot\@empty\let\@evenfoot\@empty + \def\@evenhead{\normalfont\small\rlap{\thepage}\hspace{\headlineindent}% + \hfil} + \def\@oddhead{\normalfont\small\hfil\hspace{\headlineindent}% + \llap{\thepage}} + \def\chaptermark##1{}% + \def\sectionmark##1{}% + \def\subsectionmark##1{}} + +\if@runhead\ps@headings\else +\ps@empty\fi + +\setlength\arraycolsep{1.4\p@} +\setlength\tabcolsep{1.4\p@} + +\endinput +%end of file llncs.cls diff --git a/ACSAC/paper.bib b/ACSAC/paper.bib new file mode 100644 index 0000000..ec7377e --- /dev/null +++ b/ACSAC/paper.bib @@ -0,0 +1,1204 @@ +@misc{carlini2022membership, + title={Membership Inference Attacks From First Principles}, + author={Nicholas Carlini and Steve Chien and Milad Nasr and Shuang Song and Andreas Terzis and Florian Tramer}, + year={2022}, + eprint={2112.03570}, + archivePrefix={arXiv}, + primaryClass={cs.CR} +} + +@inproceedings{salem2023sok, + title={SoK: Let the privacy games begin! A unified treatment of data inference privacy in machine learning}, + author={Salem, Ahmed and Cherubin, Giovanni and Evans, David and K{\"o}pf, Boris and Paverd, Andrew and Suri, Anshuman and Tople, Shruti and Zanella-B{\'e}guelin, Santiago}, + booktitle={2023 IEEE Symposium on Security and Privacy (SP)}, + pages={327--345}, + year={2023}, + organization={IEEE} +} + + +@inproceedings{ijcai2022p766, + title = {Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey}, + author = {Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu}, + booktitle = {Proceedings of the Thirty-First International Joint Conference on + Artificial Intelligence, {IJCAI-22}}, + publisher = {International Joint Conferences on Artificial Intelligence Organization}, + editor = {Lud De Raedt}, + pages = {5470--5477}, + year = {2022}, + month = {7}, + note = {Survey Track}, + doi = {10.24963/ijcai.2022/766}, + url = {https://doi.org/10.24963/ijcai.2022/766}, +} + +@article{accfairtradeoff, +author = {Pinzon, Carlos and Palamidessi, Catuscia and Piantanida, Pablo and Valencia, Frank}, +year = {2023}, +month = {05}, +pages = {1-30}, +title = {On the incompatibility of accuracy and equal opportunity}, +journal = {Machine Learning}, +doi = {10.1007/s10994-023-06331-y} +} + +@article{rodolfa2021empirical, + title={Empirical observation of negligible fairness--accuracy trade-offs in machine learning for public policy}, + author={Rodolfa, Kit T and Lamba, Hemank and Ghani, Rayid}, + journal={Nature Machine Intelligence}, + volume={3}, + number={10}, + pages={896--904}, + year={2021}, + publisher={Nature Publishing Group UK London} +} + +@article{zhai2022understanding, + title={Understanding why generalized reweighting does not improve over ERM}, + author={Zhai, Runtian and Dan, Chen and Kolter, Zico and Ravikumar, Pradeep}, + booktitle={International Conference on Learning Representation}, + year={2023} +} + +@article{ +veldanda2022fairness, +title={Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss}, +author={Akshaj Kumar Veldanda and Ivan Brugere and Jiahao Chen and Sanghamitra Dutta and Alan Mishler and Siddharth Garg}, +journal={Transactions on Machine Learning Research}, +issn={2835-8856}, +year={2023}, +url={https://openreview.net/forum?id=f4VyYhkRvi}, +note={} +} + +% general +% url = {https://arxiv.org/abs/2206.10923}, +@misc{arxivmichael, + doi = {10.48550/ARXIV.2206.10923}, + author = {Maheshwari, Gaurav and Perrot, Michaël}, + title = {FairGrad: Fairness Aware Gradient Descent}, + publisher = {arXiv}, + year = {2022}, +} + + +@InProceedings{classIMb1, + title = {Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding}, + author = {Guo, Lan-Zhe and Li, Yu-Feng}, + booktitle = {Proceedings of the 39th International Conference on Machine Learning}, + pages = {8082--8094}, + year = {2022}, + editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, + volume = {162}, + series = {Proceedings of Machine Learning Research}, + month = {17--23 Jul}, + publisher = {PMLR}, + pdf = {https://proceedings.mlr.press/v162/guo22e/guo22e.pdf}, + url = {https://proceedings.mlr.press/v162/guo22e.html} +} + +@article{classIMb2, + title={Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks}, + author={Sivaramakrishnan Rajaraman and Prasanth Ganesan and Sameer K. Antani}, + journal={PLoS ONE}, + year={2021}, + volume={17}, + url={https://api.semanticscholar.org/CorpusID:238259577} +} + +@misc{classIMb3, + author = {Jason Brownlee}, + title = {{A} {G}entle {I}ntroduction to {T}hreshold-{M}oving for {I}mbalanced {C}lassification - {M}achine{L}earning{M}astery.com --- machinelearningmastery.com}, + year = {}, + note = {[Accessed 31-08-2023]}, +} +%issn = {0022-0000}, +%url = {https://www.sciencedirect.com/science/article/pii/S002200009791504X}, +@article{saddlepointsolve, +title = {A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting}, +journal = {Journal of Computer and System Sciences}, +volume = {55}, +number = {1}, +pages = {119-139}, +year = {1997}, +doi = {10.1006/jcss.1997.1504}, +author = {Yoav Freund and Robert E Schapire} +} + + + +%isbn = {1595933832}, +%address = {New York, NY, USA}, +@inproceedings{curves, +author = {Davis, Jesse and Goadrich, Mark}, +title = {The Relationship between Precision-Recall and ROC Curves}, +year = {2006}, +publisher = {Association for Computing Machinery}, +doi = {10.1145/1143844.1143874}, +booktitle = {International Conference on Machine Learning}, +pages = {233–240}, +location = {Pittsburgh, Pennsylvania, USA}, +series = {ICML '06} +} + + +@inproceedings{cormode, +author = {Cormode, Graham}, +title = {Personal Privacy vs Population Privacy: Learning to Attack Anonymization}, +year = {2011}, +publisher = {Association for Computing Machinery}, +doi = {10.1145/2020408.2020598}, +booktitle = {ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, +pages = {1253–1261}, +location = {San Diego, California, USA}, +series = {KDD '11} +} + +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%issn = {0360-0300}, +%url = {https://doi.org/10.1145/3457607}, +@article{surveyfair, +author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram}, +title = {A Survey on Bias and Fairness in Machine Learning}, +year = {2021}, +volume = {54}, +number = {6}, +doi = {10.1145/3457607}, +journal = {ACM Comput. Surv.}, +month = {jul}, +articleno = {115}, +numpages = {35}, +} + + +@article{attinfSocial1, +author = {Gong, Neil Zhenqiang and Talwalkar, Ameet and Mackey, Lester and Huang, Ling and Shin, Eui Chul Richard and Stefanov, Emil and Shi, Elaine (Runting) and Song, Dawn}, +title = {Joint Link Prediction and Attribute Inference Using a Social-Attribute Network}, +year = {2014}, +publisher = {Association for Computing Machinery}, +volume = {5}, +number = {2}, +doi = {10.1145/2594455}, +journal = {ACM Trans. Intell. Syst. Technol.}, +} + + +%address = {New York, NY, USA}, + +%issn = {2471-2566}, +%url = {https://doi.org/10.1145/3154793}, +%numpages = {30}, +%%month = {jan}, +@article{attinfSocial2, +author = {Gong, Neil Zhenqiang and Liu, Bin}, +title = {Attribute Inference Attacks in Online Social Networks}, +year = {2018}, +publisher = {Association for Computing Machinery}, +volume = {21}, +number = {1}, +doi = {10.1145/3154793}, +journal = {ACM Trans. Priv. Secur.}, +articleno = {3}, +} + +%isbn = {978-1-931971-32-4}, +%address = {Austin, TX}, +%url = {https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/gong}, +%publisher = {USENIX Association}, +%month = aug, +@inproceedings {attinfSocial3, +author = {Neil Zhenqiang Gong and Bin Liu}, +title = {You Are Who You Know and How You Behave: Attribute Inference Attacks via Users{\textquoteright} Social Friends and Behaviors}, +booktitle = {USENIX Security Symposium }, +year = {2016}, +pages = {979--995}, +} + + + %URL = {https://hal.inria.fr/hal-00748162}, + %ADDRESS = {San Diego, United States}, + %MONTH = Feb, +@inproceedings{attinfSocial4, + TITLE = {{You Are What You Like! Information Leakage Through Users' Interests}}, + YEAR = {2012}, + AUTHOR = {Chaabane, Abdelberi and Acs, Gergely and Kaafar, Mohamed Ali}, + BOOKTITLE = {Network and Distributed System Security Symposium}, + PAGES = {1-14}, +} + + + + +@inproceedings{attinfSocial5, + author={Elena Zheleva and Lise Getoor}, + title={To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles}, + year={2009}, + BOOKTITLE = {International Conference on World Wide Web}, + pages={531-540}, + doi={10.1145/1526709.1526781}, +} + + + +%isbn = {9781450349130}, +%publisher = {International World Wide Web Conferences Steering Committee}, +%address = {Republic and Canton of Geneva, CHE}, +%url = {https://doi.org/10.1145/3038912.3052695}, +@inproceedings{attinfSocial6, +author = {Jia, Jinyuan and Wang, Binghui and Zhang, Le and Gong, Neil Zhenqiang}, +title = {AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields}, +year = {2017}, +doi = {10.1145/3038912.3052695}, +booktitle = {nternational Conference on World Wide Web}, +pages = {1561–1569}, +location = {Perth, Australia}, +series = {WWW '17} +} + + + +%isbn = {9781450382878}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +@inbook{dysan, +author = {Boutet, Antoine and Frindel, Carole and Gambs, S\'{e}bastien and Jourdan, Th\'{e}o and Ngueveu, Rosin Claude}, +title = {DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks}, +year = {2021}, +doi = {10.1145/3433210.3453095}, +booktitle = {ACM Asia Conference on Computer and Communications Security}, +pages = {672–686}, +serie = {ASIA CCS '21} +} + +@inproceedings{attprivacy, +author = {Zhang, Wanrong and Ohrimenko, Olga and Cummings, Rachel}, +title = {Attribute Privacy: Framework and Mechanisms}, +year = {2022}, +isbn = {9781450393522}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3531146.3533139}, +doi = {10.1145/3531146.3533139}, +booktitle = {Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency}, +pages = {757–766}, +numpages = {10}, +keywords = {Pufferfish privacy, attribute privacy, formal privacy frameworks, privacy-preserving mechanisms}, +series = {FAccT '22} +} + +%differential privacy and fairness +@inproceedings{dispvuln, +author = {Mohammad Yaghini and Bogdan Kulynych and Carmela Troncoso}, +title = {Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning}, +year = {2022}, +booktitle = {Privacy Enhancing Technologies Symposium} +} + + +%isbn = {9781450391405}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +@inproceedings{GongMIAUnfair, +author = {Zhong, Da and Sun, Haipei and Xu, Jun and Gong, Neil and Wang, Wendy Hui}, +title = {Understanding Disparate Effects of Membership Inference Attacks and Their Countermeasures}, +year = {2022}, +doi = {10.1145/3488932.3501279}, +booktitle = {ACM on Asia Conference on Computer and Communications Security}, +pages = {959–974}, +location = {Nagasaki, Japan}, +series = {ASIA CCS '22} +} + + +%sbn = {9781450311151}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/2090236.2090255}, +@inproceedings{indivfairness, +author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Richard}, +title = {Fairness through Awareness}, +year = {2012}, +doi = {10.1145/2090236.2090255}, +booktitle = {Innovations in Theoretical Computer Science Conference}, +pages = {214–226}, +location = {Cambridge, Massachusetts}, +series = {ITCS '12} +} + +@inproceedings{outIndist, +author = {Dwork, Cynthia and Kim, Michael P. and Reingold, Omer and Rothblum, Guy N. and Yona, Gal}, +title = {Outcome indistinguishability}, +year = {2021}, +isbn = {9781450380539}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3406325.3451064}, +doi = {10.1145/3406325.3451064}, +booktitle = {Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing}, +pages = {1095–1108}, +numpages = {14}, +keywords = {Prediction, Fairness, Computational Indistinguishability}, +location = {Virtual, Italy}, +series = {STOC 2021} +} + + + + + +%isbn = {9781450369367}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3351095.3372872}, +@inproceedings{dpfair, +author = {Pujol, David and McKenna, Ryan and Kuppam, Satya and Hay, Michael and Machanavajjhala, Ashwin and Miklau, Gerome}, +title = {Fair Decision Making Using Privacy-Protected Data}, +year = {2020}, +doi = {10.1145/3351095.3372872}, +booktitle = {Conference on Fairness, Accountability, and Transparency}, +pages = {189–199}, +location = {Barcelona, Spain}, +series = {FAT* '20} +} + +%url={https://ojs.aaai.org/index.php/AAAI/article/view/17193}, +%month={May}, +@article{fairprivatelagrangian, +title={Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach}, +volume={35}, +number={11}, +journal={AAAI Conference on Artificial Intelligence}, +author={Tran, Cuong and Fioretto, Ferdinando and Van Hentenryck, Pascal}, +year={2021}, +pages={9932-9939} +} + +%editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, + %series = {Proceedings of Machine Learning Research}, + %month = {09--15 Jun}, + %publisher = {PMLR}, + %pdf = {http://proceedings.mlr.press/v97/jagielski19a/jagielski19a.pdf}, + %url = {https://proceedings.mlr.press/v97/jagielski19a.html} +@InProceedings{dpfairlearn, + title = {Differentially Private Fair Learning}, + author = {Jagielski, Matthew and Kearns, Michael and Mao, Jieming and Oprea, Alina and Roth, Aaron and -Malvajerdi, Saeed Sharifi and Ullman, Jonathan}, + booktitle = {International Conference on Machine Learning}, + pages = {3000--3008}, + year = {2019}, + volume = {97}, +} + +@incollection{dpaccdisp, +title = {Differential Privacy Has Disparate Impact on Model Accuracy}, +author = {Bagdasaryan, Eugene and Poursaeed, Omid and Shmatikov, Vitaly}, +booktitle = {Advances in Neural Information Processing Systems}, +pages = {15479--15488}, +year = {2019}} + +%isbn = {978-1-939133-06-9}, +%address = {Santa Clara, CA}, +%url = {https://www.usenix.org/conference/usenixsecurity19/presentation/jayaraman}, +%publisher = {USENIX Association}, +%month = aug, +@inproceedings {dpVacc, +author = {Bargav Jayaraman and David Evans}, +title = {Evaluating Differentially Private Machine Learning in Practice}, +booktitle = {USENIX Security Symposium}, +year = {2019}, +pages = {1895--1912}, +} + +%isbn = {9781450367110}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3314183.3323847}, +@inproceedings{cummings, +author = {Cummings, Rachel and Gupta, Varun and Kimpara, Dhamma and Morgenstern, Jamie}, +title = {On the Compatibility of Privacy and Fairness}, +year = {2019}, +doi = {10.1145/3314183.3323847}, +booktitle = {Conference on User Modeling, Adaptation and Personalization}, +pages = {309–315}, +location = {Larnaca, Cyprus}, +series = {UMAP'19 Adjunct} +} + + +@techreport{ec2019ethics, + address = {Brussels}, + author = {{High-Level Expert Group on AI}}, + institution = {European Commission}, + language = {eng}, + month = apr, + title = {Ethics guidelines for trustworthy AI}, + type = {Report}, + url = {https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai}, + year = {2019} +} + +@inproceedings{nist, + title={A Taxonomy and Terminology of Adversarial Machine Learning}, + author={Elham Tabassi and Kevin J. Burns and M. Hadjimichael and Andres Molina-Markham and Julian Sexton}, + url = {https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf}, + year={2019}, + booktitle = {NIST Interagency/Internal Report} +} + +@inproceedings{dpia, +title={Art. 35 {GDPR} Data protection impact assessment}, +url={https://gdpr-info.eu/art-35-gdpr/}, +author={European Union Law}, +year={2018}, +booktitle={General Data Protection Regulation (GDPR)} } + +@article{ico, +title={{AI} auditing and impact assessment: according to the UK information commissioner’s office}, ISSN={2730-5953, 2730-5961}, url={http://link.springer.com/10.1007/s43681-021-00039-2}, DOI={10.1007/s43681-021-00039-2}, +journal={AI and Ethics}, +author={Kazim, Emre and Denny, Danielle Mendes Thame and Koshiyama, Adriano}, +year={2021}, +month={Feb} } + +@inproceedings{whitehouse, +title={Guidance for Regulation of Artificial Intelligence Applications}, +url={https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf}, +author={White House}, +year = {2020}, +booktitle={Memorandum For The Heads Of Executive Departments And Agencies} } + +%metrics + +@INPROCEEDINGS{memprivNattpriv, + author={Zhao, Benjamin Zi Hao and Agrawal, Aviral and Coburn, Catisha and Asghar, Hassan Jameel and Bhaskar, Raghav and Kaafar, Mohamed Ali and Webb, Darren and Dickinson, Peter}, + booktitle={IEEE European Symposium on Security and Privacy}, + title={On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models}, + year={2021}, + pages={232-251}, + doi={10.1109/EuroSP51992.2021.00025} +} + +@article{duddu2023sok, + title={SoK: Unintended Interactions among Machine Learning Defenses and Risks}, + author={Duddu, Vasisht and Szyller, Sebastian and Asokan, N}, + journal={arXiv preprint arXiv:2312.04542}, + year={2023} +} + + +@inproceedings{suri2023dissecting, + title={Dissecting distribution inference}, + author={Suri, Anshuman and Lu, Yifu and Chen, Yanjin and Evans, David}, + booktitle={2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)}, + pages={150--164}, + year={2023}, + organization={IEEE} +} + + +@article{de2020overview, + title={An overview of privacy in machine learning}, + author={De Cristofaro, Emiliano}, + journal={arXiv preprint arXiv:2005.08679}, + year={2020} +} + + +@article{pate2021fairness, + title={A Fairness Analysis on Private Aggregation of Teacher Ensembles}, + author={Tran, Cuong and Dinh, My H and Beiter, Kyle and Fioretto, Ferdinando}, + journal={arXiv preprint arXiv:2109.08630}, + year={2021} +} + +@article{fioretto2022differential, + title={Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey}, + author={Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu}, + journal={arXiv preprint arXiv:2202.08187}, + year={2022} +} + + +% attribute inference attacks in ML +%publisher = "Institute of Electrical and Electronics Engineers (IEEE)", +%address = "United States", +@inproceedings{zhao2021infeasibility, +title = "On the (in)feasibility of attribute inference attacks on machine learning models", +author = "Zhao, {Benjamin Zi Hao} and Aviral Agrawal and Catisha Coburn and Asghar, {Hassan Jameel} and Raghav Bhaskar and Kaafar, {Mohamed Ali} and Darren Webb and Peter Dickinson", +year = "2021", +doi = "10.1109/EuroSP51992.2021.00025", +pages = "232--251", +booktitle = "IEEE European Symposium on Security and Privacy", +serie = {EuroS&P '2021}, +} + + +%isbn = {978-1-939133-31-1}, +%address = {Boston, MA}, +%url = {https://www.usenix.org/conference/usenixsecurity22/presentation/mehnaz}, +%publisher = {USENIX Association}, +%month = aug, +@inproceedings{MehnazAttInf, +author = {Shagufta Mehnaz and Sayanton V. Dibbo and Ehsanul Kabir and Ninghui Li and Elisa Bertino}, +title = {Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models}, +booktitle = {USENIX Security Symposium}, +year = {2022}, +pages = {4579--4596}, +} + +%isbn = {9781450338325}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%%url = {https://doi.org/10.1145/2810103.2813677}, +@inproceedings{fredrikson1, +author = {Fredrikson, Matt and Jha, Somesh and Ristenpart, Thomas}, +title = {Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures}, +year = {2015}, +doi = {10.1145/2810103.2813677}, +booktitle = {ACM SIGSAC Conference on Computer and Communications Security}, +pages = {1322–1333}, +location = {Denver, Colorado, USA}, +series = {CCS '15} +} + + +%isbn = {9781931971157}, +@inproceedings{fredrikson2, +author = {Fredrikson, Matthew and Lantz, Eric and Jha, Somesh and Lin, Simon and Page, David and Ristenpart, Thomas}, +title = {Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing}, +year = {2014}, +booktitle = {USENIX Conference on Security Symposium}, +pages = {17–32}, +location = {San Diego, CA}, +series = {SEC'14} +} + +@inproceedings{Song2020Overlearning, +title={Overlearning Reveals Sensitive Attributes}, +author={Congzheng Song and Vitaly Shmatikov}, +booktitle={International Conference on Learning Representations}, +year={2020} +} + + +%isbn = {9781450384544}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3460120.3484533}, +@inproceedings{malekzadeh2021honestbutcurious, +author = {Malekzadeh, Mohammad and Borovykh, Anastasia and G\"{u}nd\"{u}z, Deniz}, +title = {Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs}, +year = {2021}, +doi = {10.1145/3460120.3484533}, +booktitle = {ACM SIGSAC Conference on Computer and Communications Security}, +pages = {825–844}, +location = {Virtual Event, Republic of Korea}, +series = {CCS '21} +} + + +@article{jayaraman2022attribute, + title={Are Attribute Inference Attacks Just Imputation?}, + author={Jayaraman, Bargav and Evans, David}, + journal={arXiv preprint arXiv:2209.01292}, + year={2022} +} + + +@inproceedings{yeom, + author={Yeom, Samuel and Giacomelli, Irene and Fredrikson, Matt and Jha, Somesh}, + booktitle={IEEE Computer Security Foundations Symposium}, + title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting}, + year={2018}, + pages={268-282}, + doi={10.1109/CSF.2018.00027} +} + + +@inproceedings{Mahajan2020DoesLS, + title={Does Learning Stable Features Provide Privacy Benefits for Machine Learning Models?}, + author={Divyat Mahajan, Shruti Tople, Amit Sharma}, + booktitle = {NeurIPS PPML Workshop}, + year={2020} +} + +@inproceedings{Malekzadeh_2021, + doi = {10.1145/3460120.3484533}, + url = {https://doi.org/10.1145%2F3460120.3484533}, + year = 2021, month = {nov}, + publisher = {{ACM}}, + author = {Mohammad Malekzadeh and Anastasia Borovykh and Deniz Gündüz}, + title = {Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers{\textquotesingle} Outputs}, + booktitle = {Proceedings of the 2021 {ACM} {SIGSAC} Conference on Computer and Communications Security}} + + + +@INPROCEEDINGS{meminf, + author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly}, + booktitle={2017 IEEE Symposium on Security and Privacy (SP)}, + title={Membership Inference Attacks Against Machine Learning Models}, + year={2017}, + pages={3-18}, + doi={10.1109/SP.2017.41}} + +@article{chang2021privacy, + title={On the Privacy Risks of Algorithmic Fairness}, + author={Hongyang Chang and R. Shokri}, + journal={{2021 }IEEE European Symposium on Security and Privacy}, + year={2021}, + pages={292-303} +} + +@article{duddu2022inferring, + title={Inferring Sensitive Attributes from Model Explanations}, + author={Duddu, Vasisht and Boutet, Antoine}, + journal={arXiv preprint arXiv:2208.09967}, + year={2022} +} + +%editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin}, + %publisher = {Curran Associates, Inc.}, + %url = {https://proceedings.neurips.cc/paper/2020/file/6b8b8e3bd6ad94b985c1b1f1b7a94cb2-Paper.pdf}, +@inproceedings{NEURIPS2020_6b8b8e3b, + author = {Zhao, Han and Chi, Jianfeng and Tian, Yuan and Gordon, Geoffrey J}, + booktitle = {Advances in Neural Information Processing Systems}, + pages = {9485--9496}, + title = {Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation}, + volume = {33}, + year = {2020} +} + + + +@ARTICLE{8515092, +author={S. A. {Osia} and A. {Taheri} and A. S. {Shamsabadi} and K. {Katevas} and H. {Haddadi} and H. R. {Rabiee}}, +journal={IEEE Transactions on Knowledge and Data Engineering}, +title={Deep Private-Feature Extraction}, +year={2020}, +volume={32}, +number={1}, +pages={54-66}, +} + + + +%eprint = {1707.00075} +@article{advfair, + author = {Alex Beutel and Jilin Chen and Zhe Zhao and Ed H. Chi}, + title = {Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations}, + year = {2017}, + publisher = {arXiv}, + doi = {10.48550/ARXIV.1707.00075}, +} + +%property inference attack + +@article{propinf, + title={Dataset-Level Attribute Leakage in Collaborative Learning}, + author={Zhang, Wanrong and Tople, Shruti and Ohrimenko, Olga}, + journal={arXiv:2006.07267}, + year={2020} +} + +%month = sep, +@article{propinf2, +author = {Ateniese, Giuseppe and Mancini, Luigi V. and Spognardi, Angelo and Villani, Antonio and Vitali, Domenico and Felici, Giovanni}, +title = {Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers}, +year = {2015}, +volume = {10}, +number = {3}, +journal = {Int. J. Secur. Netw.}, +pages = {137–150} +} + + +%isbn = {9781450356930}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3243734.3243834}, +@inproceedings{propinf3, +author = {Ganju, Karan and Wang, Qi and Yang, Wei and Gunter, Carl A. and Borisov, Nikita}, +title = {Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations}, +year = {2018}, +doi = {10.1145/3243734.3243834}, +booktitle = {ACM SIGSAC Conference on Computer and Communications Security}, +pages = {619–633}, +location = {Toronto, Canada}, +series = {CCS '18} +} + +@article{propinf4, + title={Formalizing and Estimating Distribution Inference Risks}, + author={Suri, Anshuman and Evans, David}, + journal={Proceedings on Privacy Enhancing Technologies}, + year={2022} +} + +@inproceedings{fedinference, +author={L. {Melis} and C. {Song} and E. {De Cristofaro} and V. {Shmatikov}}, +booktitle={IEEE Symposium on Security and Privacy}, +title={Exploiting Unintended Feature Leakage in Collaborative Learning}, +year={2019}, +pages={691-706} +} + +@INPROCEEDINGS {ferryExploit, +author = {J. Ferry and U. Aivodji and S. Gambs and M. Huguet and M. Siala}, +booktitle = {2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)}, +title = {Exploiting Fairness to Enhance Sensitive Attributes Reconstruction}, +year = {2023}, +volume = {}, +issn = {}, +pages = {18-41}, +keywords = {training;measurement;learning systems;privacy;pipelines;training data;machine learning}, +doi = {10.1109/SaTML54575.2023.00012}, +url = {https://doi.ieeecomputersociety.org/10.1109/SaTML54575.2023.00012}, +publisher = {IEEE Computer Society}, +address = {Los Alamitos, CA, USA}, +month = {feb} +} + + + + +% defences against attribute inference attacks + +@inproceedings{10.5555/3042817.3042973, +author = {Zemel, Richard and Wu, Yu and Swersky, Kevin and Pitassi, Toniann and Dwork, Cynthia}, +title = {Learning Fair Representations}, +year = {2013}, +booktitle = {International Conference on Machine Learning}, +serie = {ICML '13}, +} + +%month = jan, +@article{10.5555/3122009.3208010, +author = {Hamm, Jihun}, +title = {Minimax Filter: Learning to Preserve Privacy from Inference Attacks}, +year = {2017}, +volume = {18}, +number = {1}, +journal = {J. Mach. Learn. Res.}, +pages = {4704–4734} +} + +@inproceedings{10.5555/3327546.3327583, +author = {Moyer, Daniel and Gao, Shuyang and Brekelmans, Rob and Steeg, Greg Ver and Galstyan, Aram}, +title = {Invariant Representations without Adversarial Training}, +year = {2018}, +booktitle = {Advances in Neural Information Processing Systems} +} + +@inproceedings{10.5555/3294771.3294827, +author = {Xie, Qizhe and Dai, Zihang and Du, Yulun and Hovy, Eduard and Neubig, Graham}, +title = {Controllable Invariance through Adversarial Feature Learning}, +year = {2017}, +booktitle = {Advances in Neural Information Processing Systems} +} + +@InProceedings{pmlr-v80-madras18a, + title = {Learning Adversarially Fair and Transferable Representations}, + author = {Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard}, + pages = {3384--3393}, + year = {2018}, + volume = {80}, + booktitle = {Proceedings of Machine Learning Research}, +} + +@inproceedings{censoringadv, +title = "Censoring Representations with an Adversary", +author = "Harrison Edwards and Amos Storkey", +year = "2016", +booktitle = {International Conference on Learning Representations} +} +@inproceedings{NIPS2017_48ab2f9b, + author = {Louppe, Gilles and Kagan, Michael and Cranmer, Kyle}, + booktitle = {Advances in Neural Information Processing Systems}, + editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}, + pages = {}, + publisher = {Curran Associates, Inc.}, + title = {Learning to Pivot with Adversarial Networks}, + url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/48ab2f9b45957ab574cf005eb8a76760-Paper.pdf}, + volume = {30}, + year = {2017} +} + + +%isbn = {9781450360128}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3278721.3278779}, +@inproceedings{debiase, +author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret}, +title = {Mitigating Unwanted Biases with Adversarial Learning}, +year = {2018}, +doi = {10.1145/3278721.3278779}, +booktitle = {AAAI/ACM Conference on AI, Ethics, and Society}, +pages = {335–340}, +location = {New Orleans, LA, USA}, +series = {AIES '18} +} + + %month = {10}, +%pages = {}, +@article{preprocessing, +author = {Kamiran, Faisal and Calders, Toon}, +year = {2011}, +title = {Data Pre-Processing Techniques for Classification without Discrimination}, +volume = {33}, +journal = {Knowledge and Information Systems}, +doi = {10.1007/s10115-011-0463-8} +} + +%series = {Proceedings of Machine Learning Research}, + %month = {10--15 Jul}, + %publisher = {PMLR}, + %pdf = {http://proceedings.mlr.press/v80/agarwal18a/agarwal18a.pdf}, + %url = {https://proceedings.mlr.press/v80/agarwal18a.html}, +@InProceedings{reductions, + title = {A Reductions Approach to Fair Classification}, + author = {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna}, + booktitle = {International Conference on Machine Learning}, + pages = {60--69}, + year = {2018}, + volume = {80}, +} + +@article{kifer2014pufferfish, +author = {Kifer, Daniel and Machanavajjhala, Ashwin}, +title = {Pufferfish: A framework for mathematical privacy definitions}, +year = {2014}, +issue_date = {January 2014}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +volume = {39}, +number = {1}, +issn = {0362-5915}, +url = {https://doi.org/10.1145/2514689}, +doi = {10.1145/2514689}, +journal = {ACM Trans. Database Syst.}, +month = {jan}, +articleno = {3}, +numpages = {36}, +keywords = {Privacy, differential privacy} +} + +@inproceedings{song2017pufferfish, + title={Pufferfish privacy mechanisms for correlated data}, + author={Song, Shuang and Wang, Yizhen and Chaudhuri, Kamalika}, + booktitle={Proceedings of the 2017 ACM International Conference on Management of Data}, + pages={1291--1306}, + year={2017} +} + +@article{grinsztajn2022tree, + title={Why do tree-based models still outperform deep learning on typical tabular data?}, + author={Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l}, + journal={Advances in neural information processing systems}, + volume={35}, + pages={507--520}, + year={2022} +} + + + +@inproceedings {attriguard, +author = {Jinyuan Jia and Neil Zhenqiang Gong}, +title = {AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning}, +booktitle = {USENIX Security}, +year = {2018}, +pages = {513--529}, +} + + +% fairness metrics + +@article{fairmetric, +author = {Muhammad Bilal Zafar and Isabel Valera and Manuel Gomez-Rodriguez and Krishna P. Gummadi}, +title = {Fairness Constraints: A Flexible Approach for Fair Classification}, +journal = {Journal of Machine Learning Research}, +year = {2019}, +volume = {20}, +number = {75}, +pages = {1-42} +} + +@inproceedings{fairmetric2, +author = {Hardt, Moritz and Price, Eric and Srebro, Nathan}, +title = {Equality of Opportunity in Supervised Learning}, +year = {2016}, +booktitle = {Advances in Neural Information Processing Systems}, +pages = {3323–3331} +} + +@article{fairjustice, +author = {Alikhademi, Kiana and Drobina, Emma and Prioleau, Diandra and Richardson, Brianna and Purves, Duncan and Gilbert, Juan E.}, +title = {A Review of Predictive Policing from the Perspective of Fairness}, +year = {2022}, +issue_date = {Mar 2022}, +publisher = {Kluwer Academic Publishers}, +address = {USA}, +volume = {30}, +number = {1}, +issn = {0924-8463}, +url = {https://doi.org/10.1007/s10506-021-09286-4}, +doi = {10.1007/s10506-021-09286-4}, +journal = {Artif. Intell. Law}, +month = {mar}, +pages = {1–17}, +numpages = {17}, +keywords = {Predictive policing, Algorithmic fairness, Fairness, AI in criminal justice} +} + + +@article{folk, + title={Retiring Adult: New Datasets for Fair Machine Learning}, + author={Ding, Frances and Hardt, Moritz and Miller, John and Schmidt, Ludwig}, + journal={Advances in Neural Information Processing Systems}, + volume={34}, + year={2021} +} + +@inproceedings{ + SDV, + title={The Synthetic data vault}, + author={Patki, Neha and Wedge, Roy and Veeramachaneni, Kalyan}, + booktitle={IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, + year={2016}, + pages={399-410}, + doi={10.1109/DSAA.2016.49}, + month={Oct} +} + +@misc{dpbad, + doi = {10.48550/ARXIV.1104.3913}, + + url = {https://arxiv.org/abs/1104.3913}, + + author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Rich}, + + keywords = {Computational Complexity (cs.CC), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences}, + + title = {Fairness Through Awareness}, + + publisher = {arXiv}, + + year = {2011}, + + copyright = {arXiv.org perpetual, non-exclusive license} +} + +@INPROCEEDINGS{fairlog, + + author={Radovanović, Sandro and Petrović, Andrija and Delibašić, Boris and Suknović, Milija}, + + booktitle={2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)}, + + title={Enforcing fairness in logistic regression algorithm}, + + year={2020}, + + volume={}, + + number={}, + + pages={1-7}, + + doi={10.1109/INISTA49547.2020.9194676}} + + +@misc{fairreg, + title={Fair Regression: Quantitative Definitions and Reduction-based Algorithms}, + author={Alekh Agarwal and Miroslav Dudík and Zhiwei Steven Wu}, + year={2019}, + eprint={1905.12843}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + + + +@InProceedings{fairaudit1, + title = {Blind Justice: Fairness with Encrypted Sensitive Attributes}, + author = {Kilbertus, Niki and Gascon, Adria and Kusner, Matt and Veale, Michael and Gummadi, Krishna and Weller, Adrian}, + booktitle = {Proceedings of the 35th International Conference on Machine Learning}, + pages = {2630--2639}, + year = {2018}, + editor = {Dy, Jennifer and Krause, Andreas}, + volume = {80}, + series = {Proceedings of Machine Learning Research}, + month = {10--15 Jul}, + publisher = {PMLR}, + pdf = {http://proceedings.mlr.press/v80/kilbertus18a/kilbertus18a.pdf}, + url = {https://proceedings.mlr.press/v80/kilbertus18a.html}, +} + + +@inproceedings{fairaudit2, +author = {Park, Saerom and Kim, Seongmin and Lim, Yeon-sup}, +title = {Fairness Audit of Machine Learning Models with Confidential Computing}, +year = {2022}, +isbn = {9781450390965}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3485447.3512244}, +doi = {10.1145/3485447.3512244}, +booktitle = {Proceedings of the ACM Web Conference 2022}, +pages = {3488–3499}, +numpages = {12}, +keywords = {Confidential computing, Algorithmic audit, Security and privacy, Fairness}, +location = {Virtual Event, Lyon, France}, +series = {WWW '22} +} + +@inproceedings{fairaudit3, +author = {Segal, Shahar and Adi, Yossi and Pinkas, Benny and Baum, Carsten and Ganesh, Chaya and Keshet, Joseph}, +title = {Fairness in the Eyes of the Data: Certifying Machine-Learning Models}, +year = {2021}, +isbn = {9781450384735}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3461702.3462554}, +doi = {10.1145/3461702.3462554}, +booktitle = {Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society}, +pages = {926–935}, +numpages = {10}, +keywords = {machine-learning, cryptography, privacy, fairness}, +location = {Virtual Event, USA}, +series = {AIES '21} +} + +@article{yadav2024fairproof, + title={FairProof: Confidential and Certifiable Fairness for Neural Networks}, + author={Yadav, Chhavi and Chowdhury, Amrita Roy and Boneh, Dan and Chaudhuri, Kamalika}, + journal={arXiv preprint arXiv:2402.12572}, + year={2024} +} + +@inproceedings{khedr2023certifair, + title={Certifair: A framework for certified global fairness of neural networks}, + author={Khedr, Haitham and Shoukry, Yasser}, + booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, + volume={37}, + number={7}, + pages={8237--8245}, + year={2023} +} + +@article{urban20, +author = {Urban, Caterina and Christakis, Maria and W\"{u}stholz, Valentin and Zhang, Fuyuan}, +title = {Perfectly parallel fairness certification of neural networks}, +year = {2020}, +issue_date = {November 2020}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +volume = {4}, +number = {OOPSLA}, +journal = {Proc. ACM Program. Lang.}, +month = {nov}, +articleno = {185}, +numpages = {30}, +keywords = {Static Analysis, Neural Networks, Fairness, Abstract Interpretation} +} + +@inproceedings{ +chugg2023auditing, +title={Auditing Fairness by Betting}, +author={Ben Chugg and Santiago Cortes-Gomez and Bryan Wilder and Aaditya Ramdas}, +booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, +year={2023}, +url={https://openreview.net/forum?id=EEVpt3dJQj} +} + +@inproceedings{yan2022active, + title={Active fairness auditing}, + author={Yan, Tom and Zhang, Chicheng}, + booktitle={International Conference on Machine Learning}, + pages={24929--24962}, + year={2022}, + organization={PMLR} +} + +@article{de2024fairness, + title={Fairness Auditing with Multi-Agent Collaboration}, + author={de Vos, Martijn and Dhasade, Akash and Bourr{\'e}e, Jade Garcia and Kermarrec, Anne-Marie and Merrer, Erwan Le and Rottembourg, Benoit and Tredan, Gilles}, + journal={arXiv preprint arXiv:2402.08522}, + year={2024} +} + +@inproceedings{ghosh2022algorithmic, + title={Algorithmic fairness verification with graphical models}, + author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S}, + booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, + volume={36}, + number={9}, + pages={9539--9548}, + year={2022} +} + +@inproceedings{ghosh2023biased, + title={“How Biased are Your Features?”: Computing Fairness Influence Functions with Global Sensitivity Analysis}, + author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S}, + booktitle={Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency}, + pages={138--148}, + year={2023} +} + +@article{FairSquare, +author = {Albarghouthi, Aws and D'Antoni, Loris and Drews, Samuel and Nori, Aditya V.}, +title = {FairSquare: probabilistic verification of program fairness}, +year = {2017}, +issue_date = {October 2017}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +volume = {1}, +number = {OOPSLA}, +url = {https://doi.org/10.1145/3133904}, +doi = {10.1145/3133904}, +journal = {Proc. ACM Program. Lang.}, +month = {oct}, +articleno = {80}, +numpages = {30}, +keywords = {Algorithmic Fairness, Probabilistic Inference, Probabilistic Programming} +} + +@article{saleiro2018aequitas, + title={Aequitas: A bias and fairness audit toolkit}, + author={Saleiro, Pedro and Kuester, Benedict and Hinkson, Loren and London, Jesse and Stevens, Abby and Anisfeld, Ari and Rodolfa, Kit T and Ghani, Rayid}, + journal={arXiv preprint arXiv:1811.05577}, + year={2018} +} + +@article{bastani2019probabilistic, + title={Probabilistic verification of fairness properties via concentration}, + author={Bastani, Osbert and Zhang, Xin and Solar-Lezama, Armando}, + journal={Proceedings of the ACM on Programming Languages}, + volume={3}, + number={OOPSLA}, + pages={1--27}, + year={2019}, + publisher={ACM New York, NY, USA} +} + +@article{adler2018auditing, + title={Auditing black-box models for indirect influence}, + author={Adler, Philip and Falk, Casey and Friedler, Sorelle A and Nix, Tionney and Rybeck, Gabriel and Scheidegger, Carlos and Smith, Brandon and Venkatasubramanian, Suresh}, + journal={Knowledge and Information Systems}, + volume={54}, + pages={95--122}, + year={2018}, + publisher={Springer} +} + +@inproceedings{black2020fliptest, + title={Fliptest: fairness testing via optimal transport}, + author={Black, Emily and Yeom, Samuel and Fredrikson, Matt}, + booktitle={Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency}, + pages={111--121}, + year={2020} +} + +@article{Justicia, +title={Justicia: A Stochastic SAT Approach to Formally Verify Fairness}, +volume={35}, +url={https://ojs.aaai.org/index.php/AAAI/article/view/16925}, DOI={10.1609/aaai.v35i9.16925}, +number={9}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.}, year={2021}, month={May}, pages={7554-7563} }
\ No newline at end of file diff --git a/ACSAC/proofs/proof_advdebias.tex b/ACSAC/proofs/proof_advdebias.tex new file mode 100644 index 0000000..06212a0 --- /dev/null +++ b/ACSAC/proofs/proof_advdebias.tex @@ -0,0 +1,30 @@ +Definition~\ref{def:dp} of \dempar can be written synthetically as the following property: +$P_{\hat{Y},S}=P_{\hat{Y}}\otimes P_{S}$. +Where $P_{\hat{Y}}\otimes P_{S}$ is the product measure defined as the unique measure on +$\mathcal{P}(\mathcal{Y})\times\mathcal{P}(\mathcal{S})$ such that +$\forall y\in\mathcal{P}(\mathcal{Y})\forall s\in\mathcal{P}(\mathcal{S})\quad P_{\hat{Y}}\otimes P_{S}(y\times s) = P_{\hat{Y}}(y)P_{S}(s)$. +This is equivalent to definition~\ref{def:dp} for binary labels and sensitive attribute but more general because when $\hat{Y}$ is not binary as in soft labels, this new definition is well defined. +% We write formally +\begin{definition} +\label{def:dps} + $\hat{Y}$ satisfies extended \dempar for $S$ if and only if: $P_{\hat{Y},S}=P_{\hat{Y}}\otimes P_{S}$. +\end{definition} +This definition is the same as the statistical parity introduced for fair regression~\cite{fairreg}. +Note that we can not derive a quantity similar to \demparlevel with this definition but this extended \dempar assures indistinguishably of the sensitive attribute when looking at the soft labels. +We have the following theorem: +\begin{theorem}\label{th:advdebias} + The following propositions are equivalent: ``$\hat{Y}_s$ is independent of $S$'' and ``Balanced accuracy of \aia in \ref{tm:soft} is $\frac{1}{2}$'' +\end{theorem} +\begin{proof} +Let's show that it is equivalent to say "all attack models have a balanced accuracy of 0.5" and "the target model satisfies extended demographic parity". +{\footnotesize + \begin{align*} + &\forall a~P(\hat{Y}\in a^{-1}(\{0\})|S=0)+P(\hat{Y}\in a^{-1}(\{1\})|S=1) = 1\\ + \Leftrightarrow&\forall a~P(\hat{Y}\in a^{-1}(\{0\})|S=0)=P(\hat{Y}\in a^{-1}(\{0\})|S=1)\\ + \Leftrightarrow&\forall A~P(\hat{Y}\in A)|S=0)=P(\hat{Y}\in A|S=1) \\ + \Leftrightarrow &P_{\hat{Y},S}=P_{\hat{Y}}\otimes P_{S} + \end{align*} + } +\end{proof} + +%In conclusion, with extended \dempar we can not unconditionally bound the balanced accuracy of the attack without introducing distances in the space of distributions, but it gives us a condition to protect the sensitive attribute in case of an adversary gaining access to soft labels (AS).
\ No newline at end of file diff --git a/ACSAC/proofs/proof_egd_dp.tex b/ACSAC/proofs/proof_egd_dp.tex new file mode 100644 index 0000000..d0f23f6 --- /dev/null +++ b/ACSAC/proofs/proof_egd_dp.tex @@ -0,0 +1,33 @@ +\begin{theorem} +\label{th:dpgood} +Maximum attack accuracy achievable by \aia in \ref{tm:hard} is equal to $\frac{1}{2}(1+\text{\demparlevel of }\targetmodel)$. +\end{theorem} +\begin{proof} +The set $B$ of function from $\{0,1\}$ to $\{0,1\}$ contains four elements: $b_0=0$, $b_1=id$, $b_2=1-id$ and $b,3=1$, where $\forall x, id(x) = x$. +For every $b\in B$ the balanced \aia accuracy is +$BA(b) = \frac{1}{2}(P(b\circ \hat{Y}=0|S=0) + P(b\circ \hat{Y}=1|S=1))$. +We have $BA(b_0) = BA(b_3) = \frac{1}{2}$, hence, we can discard those elements when solving the attack optimisation problem. +This problem writes $\text{max}_{b\in B}B(A(b)) = \text{max}(BA(b_1), BA(b_2))$. +We remark that $b_1\circ \hat{Y}=\hat{Y}$ and $b_2\circ \hat{Y}=1 - \hat{Y}$. +Hence, +{\footnotesize +\begin{align*} + BA(b_1) &= \frac{1}{2}(P(\hat{Y}=0|S=0) + P(\hat{Y}=1|S=1))\\ + &=\frac{1}{2}(1+P(\hat{Y}=1|S=1) - P(\hat{Y}=1|S=0))\\ + BA(b_2)&=\frac{1}{2}(1+P(\hat{Y}=1|S=0) - P(\hat{Y}=1|S=1)) +\end{align*} +} +Thus, +{\footnotesize +\begin{align*} + &\text{max}_{b\in B}BA(b) + = \frac{1}{2}\left(1+\text{max}\left( + \begin{matrix} + P(\hat{Y}=0|S=0) -P(\hat{Y}=1|S=1)\\ + P(\hat{Y}=1|S=0) -P(\hat{Y}=0|S=1) + \end{matrix} + \right)\right)\\ + =&\frac{1}{2}(1+|P(\hat{Y}=1|S=1) - P(\hat{Y}=1|S=0)|) +\end{align*} +} +\end{proof} diff --git a/ACSAC/proofs/proof_egd_eo.tex b/ACSAC/proofs/proof_egd_eo.tex new file mode 100644 index 0000000..435add2 --- /dev/null +++ b/ACSAC/proofs/proof_egd_eo.tex @@ -0,0 +1,35 @@ +\begin{theorem} +\label{th:eoo} +If $\hat{Y}$ satisfies \eo for $Y$ and $S$ then the balanced accuracy of \aia in \ref{tm:hard} is $\frac{1}{2}$ iff $Y$ is independent of $S$ or $\hat{Y}$ is independent of $Y$. +\end{theorem} + +\begin{proof} +Let $\attackmodel$ be the attack model trained for AS: $\hat{S}=a\circ \hat{Y}$. +By the total probability formula +{\footnotesize +\begin{align*} +P(\hat{S}=0|S=0)=&P(\hat{S}=0|S=0Y=0)P(Y=0|S=0)\\ ++&P(\hat{S}=0|S=0Y=1)P(Y=1|S=0) +\end{align*} +} +and as well +{\footnotesize +\begin{align*} +P(\hat{S}=1|S=1)=&P(\hat{S}=1|S=1Y=0)P(Y=0|S=1)\\ + +&P(\hat{S}=1|S=1Y=1)P(Y=1|S=1) +\end{align*} +} +Then we substitute those terms in the definition of the balanced accuracy of $\targetmodel$. +{\footnotesize +\begin{align*} + &\frac{P(\hat{S}=0|S=0)+P(\hat{S}=1|S=1)}{2}\\ + =&\frac{1}{2}+\frac{1}{2}\left(P(Y=0|S=0)-P(Y=0|S=1)\right)\\ + &\left(P(\hat{Y}\in \attackmodel^{-1}(\{1\})|S=1Y=0) - + P(\hat{Y}\in \attackmodel^{-1}(\{1\})|S=1Y=1)\right) +\end{align*} +} +The balanced accuracy is equal to 0.5 if and only if $P(Y=0|S=0)=P(Y=0|S=1)$ +or $\forall \attackmodel~P(\hat{Y}\in \attackmodel^{-1}(\{1\})|S=1Y=0)=P(\hat{Y}\in \attackmodel^{-1}(\{1\})|S=1Y=1)$. +The first term indicates that $Y$ is independent of $S$ and the second term indicates that $S=1$ the $\targetmodel$ random guess utility. +We can do the same computing for $S=0$ and obtain a similar conclusion. +\end{proof}
\ No newline at end of file diff --git a/ACSAC/tables/tab_attack.tex b/ACSAC/tables/tab_attack.tex new file mode 100644 index 0000000..51974df --- /dev/null +++ b/ACSAC/tables/tab_attack.tex @@ -0,0 +1,30 @@ +\begin{table}[!htb] +%\vspace{-3mm} +\caption{\adaptiveAIAHard and \adaptiveAIASoft outperform their respective baselines. We report attack accuracy over ten runs.} +\begin{center} +%\footnotesize +\scriptsize +\begin{tabular}{ l | c | c } +\hline +\rowcolor{LightCyan} & \multicolumn{2}{c}{\ref{tm:hard}}\\ +\rowcolor{LightCyan} \textbf{Dataset} & \textbf{Baseline ($\upsilon$=0.50)} & \textbf{\adaptiveAIAHard}\\ +\rowcolor{LightCyan} & \textbf{\race} | \textbf{\sex}& \textbf{\race} | \textbf{\sex}\\ +\textbf{\census} & 0.50 $\pm$ 0.00 | 0.50 $\pm$ 0.00& \textbf{0.56 $\pm$ 0.01} | \textbf{0.58 $\pm$ 0.01} \\ +\textbf{\compas}& \textbf{0.62 $\pm$ 0.03} | 0.50 $\pm$ 0.00& \textbf{0.62 $\pm$ 0.03} | \textbf{0.57 $\pm$ 0.03} \\ +\textbf{\meps} & 0.51 $\pm$ 0.01 | \textbf{0.55 $\pm$ 0.02} & \textbf{0.53 $\pm$ 0.01} | \textbf{0.55 $\pm$ 0.01} \\ +\textbf{\lfw} & 0.59 $\pm$ 0.00 | 0.64 $\pm$ 0.15& \textbf{0.61 $\pm$ 0.11} | \textbf{0.78 $\pm$ 0.05} \\ +\hline +\rowcolor{LightCyan} & \multicolumn{2}{c}{\ref{tm:soft}} \\ +\rowcolor{LightCyan} \textbf{Dataset} & \textbf{Baseline ($\upsilon$=0.50)} & \textbf{\adaptiveAIASoft} \\ + \rowcolor{LightCyan} & \textbf{\race} | \textbf{\sex} & \textbf{\race} | \textbf{\sex}\\ +\hline +\textbf{\census}& 0.50 $\pm$ 0.02 | 0.56 $\pm$ 0.04 & \textbf{0.61 $\pm$ 0.02} | \textbf{0.68 $\pm$ 0.01} \\ +\textbf{\compas}& \textbf{0.62 $\pm$ 0.03} | 0.50 $\pm$ 0.00 & \textbf{0.62 $\pm$ 0.03} | \textbf{0.57 $\pm$ 0.03} \\ +\textbf{\meps} & 0.52 $\pm$ 0.02 | 0.55 $\pm$ 0.02 & \textbf{0.60 $\pm$ 0.02} | \textbf{0.62 $\pm$ 0.02}\\ +\textbf{\lfw} & 0.50 $\pm$ 0.10 | \textbf{0.77 $\pm$ 0.07} & \textbf{0.61 $\pm$ 0.10} | \textbf{0.79 $\pm$ 0.05}\\ +\hline +\end{tabular} +\end{center} +\label{tab:global_threshold_withoutsattr} +%\vspace{-5mm} +\end{table}
\ No newline at end of file diff --git a/ACSAC/tables/tab_datasets.tex b/ACSAC/tables/tab_datasets.tex new file mode 100644 index 0000000..3dfe024 --- /dev/null +++ b/ACSAC/tables/tab_datasets.tex @@ -0,0 +1,17 @@ +\begin{table}[htb] +\caption{Summary of dataset splits: $\traindata$ to train $\targetmodel$, $\testdata$ to evaluate $\targetmodel$, $\auxtraindata$ to train $\attackmodel$, and $\auxtestdata$ to evaluate $\attackmodel$.} +\footnotesize +\begin{center} +\begin{tabular}{ l | c | c | c | c} +\hline + \textbf{Dataset} & $\traindata$ & $\testdata$ & $\auxtraindata$ & $\auxtestdata$\\ + \hline + \textbf{\census} & 24,752 & 6,188 & 4,950 & 1,238 \\ + \textbf{\compas} & 4,937 & 1,235 & 988 & 247\\ + \textbf{\meps} & 12,664 & 3,166 & 2,532 & 634\\ + \textbf{\lfw} & 10514 & 2629 & 2103 & 526\\ + \hline +\end{tabular} +\end{center} +\label{tab:summary} +\end{table}
\ No newline at end of file diff --git a/ACSAC/tables/tab_summary.tex b/ACSAC/tables/tab_summary.tex new file mode 100644 index 0000000..f9598a4 --- /dev/null +++ b/ACSAC/tables/tab_summary.tex @@ -0,0 +1,32 @@ +\setlength\tabcolsep{3pt} +\begin{table*}[!htb] +\caption{Comparison of prior \aia{s}: attack vector exploited (e.g., $\targetmodel(X(\omega))$, $X(\omega)$, $Y(\omega)$, distribution over $S$ ($P_S$) and confusion matrix $C(Y,\targetmodel\circ X)$), whether $S$ is censored, i.e., included in $\traindata$ and inputs, whether \aia{s} account for class imbalance in $S$, whether \adv is active or passive and whether the threat model is blackbox or whitebox.} +\begin{center} +\footnotesize +% \resizebox{\textwidth}{!}{% +\begin{tabular}{ |c|c|c|c|c|c| } + \hline + \rowcolor{LightCyan} + \textbf{Literature} & \textbf{Attack Vector} & \textbf{Is $S$ censored?} & \textbf{Imbalance in $S$?} & \textbf{\adv} & \textbf{Threat Model} \\ + \hline + \rowcolor{LightCyan} + \multicolumn{6}{|c|}{\textbf{Imputation-based Attacks}}\\ + \hline + \textbf{Fredrikson et al.}~\cite{fredrikson2} & $X$, $Y$, $\targetmodel\circ X$, \textbf{$P_S$}, $C(Y,\targetmodel\circ X$) & $\checkmark$ & $\times$ & Passive & Blackbox\\ + \textbf{Yeom et al.}~\cite{yeom} & $X$, $Y$, $\targetmodel$, \textbf{$P_S$} & $\checkmark$ & $\times$ & Passive & Blackbox\\ + \textbf{Mehnaz et al.}~\cite{MehnazAttInf} & $X$, $Y$, $\targetmodel$, \textbf{$P_S$}, $C(Y,\targetmodel\circ X)$ & $\checkmark$ & $\times$ & Passive & Blackbox\\ + \textbf{Jayaraman and Evans}~\cite{jayaraman2022attribute} & $X$, $Y$, $\targetmodel$, $P_S$, $C(Y,\targetmodel\circ X)$ & $\times$, $\checkmark$ & $\times$ & Passive & Whitebox\\ + \hline + \rowcolor{LightCyan} + \multicolumn{6}{|c|}{\textbf{Representation-based Attacks}}\\ + \hline + \textbf{Song et al.}~\cite{Song2020Overlearning} & $\targetmodel\circ X$ & $\times$ & $\times$ & Passive & Both\\ + \textbf{Mahajan et al.}~\cite{Mahajan2020DoesLS} & $\targetmodel\circ X$ & $\checkmark$ & $\times$ & Passive & Blackbox\\ + \textbf{Malekzadeh et al.}~\cite{malekzadeh2021honestbutcurious} & $\targetmodel\circ X$ & $\times$ & $\times$ & Active & Blackbox\\ + % \textbf{Our Work} & $\targetmodel\circ X$ & $\times$, $\checkmark$ & $\checkmark$ & Passive & Blackbox \\ + \hline +\end{tabular} +% } +\end{center} +\label{tab:summary} +\end{table*}
\ No newline at end of file diff --git a/aia/fair_reg.tex b/aia/fair_reg.tex new file mode 100644 index 0000000..2f2a0e0 --- /dev/null +++ b/aia/fair_reg.tex @@ -0,0 +1,46 @@ +A la Section~\ref{sec:background-eq} nous avons introduits la notion de \textit{demographic parity} (DemPar). +Dans le cas d'un classifieur binaire ($\hat{Y}$) avec attribut binaire ($S$), nous pouvons calculer à quel point le classifieur est proche d'être DemPar avec la quantité suivante : +\begin{equation*} + \text{DemParLvl} = |P(\hat{Y}=1|S=0) - P(\hat{Y}=1|S=1)| +\end{equation*} +C'est l'écart de prédiction positive entre la classe majoritair(par exemple les blancs, le hommes, ...) et la classe minoritaire (les noirs, les femmes, ...). +\begin{propriete} + \label{prop:aia-dpl0} + Un classifieur qui satisfat la \textit{demographic parity} a n DemParLvl égale à zéro. +\end{propriete} +La démonstration est triviale à partir de la Définition~\ref{def:background-eq-dp}. + +DemPar est équivalante à dire que la prédiction du modèle est idépendante de l'attribut sensible. +Nous remarquons que cette définition n'est ni restrainte à des problème de classification, ni à des attribute senssibles binaires ni même à des attribut sensibles qui prennent leurs valeur dans un ensemble fini. +Ainsi nous définissons la notion suivante: +\begin{definition}{\textit{Démographic parity} généralisée.} + \label{def:aia-dempargen} +Soit $(\Omega,\mathcal{T},P$) un espace probabilisé. +Soient $(E,\mathcal{E})$, $(F,\mathcal{F})$ et $(G,\mathcal{G})$ des espaces mesurables. +Soient les variables aléatoires suivantes : +\begin{itemize} + \item $X:(\Omega,\mathcal{T})\rightarrow (E,\mathcal{E})$ + \item $Y:(\Omega,\mathcal{T})\rightarrow (F,\mathcal{F})$ + \item $S:(\Omega,\mathcal{T})\rightarrow (G,\mathcal{G})$ + \item $f:(E,\mathcal{E})\rightarrow (F,\mathcal{F})$ +\end{itemize} +Alors $f$ satisfait la \textit{demographic parity} généralisée si et seulement si +\begin{equation*} + P_{f\circ X,S} = P_{f\circ X}\otimes P_S +\end{equation*} +Dit autrement, si et seulement si le classifieur $f$ est un CCA pour prédire $S$ à partire de $X$. +\end{definition} + +\begin{propriete} + Si un classifieur binaire satisfait la \textit{demographic parity} généralisée alors il satisfait la démographic parity. +\end{propriete} + +\begin{proof} + En gardant les objets définits dans la Définition~\ref{def:aia-dempargen}, supposons que $f$ satisfasse la \textit{demographic parity} généralisée. + Alors, en notant $\hat{Y} = f\circ X$, comme $\mathcal{G} = \mathcal{F}=\mathcal{P}(\{0,1\})$, nous avons bien + \begin{equation*} + P(\hat{Y}=1\mid S=0) = P(\hat{Y}=1\mid S=1) + \end{equation*} +\end{proof} + +Ainsi grâce à la Propriété~\ref{prop:aia-dpl0} nous savons que si un classifieur satisfait la \textit{demographic parity} généralisée, alors il a un DemParLvl égale à 0. diff --git a/aia/intro.tex b/aia/intro.tex new file mode 100644 index 0000000..b921ffc --- /dev/null +++ b/aia/intro.tex @@ -0,0 +1,20 @@ +Nous avons vu à la Section~\ref{} que, pour imposer l'équitée à un modèle, nous pouvons utiliser différentes méthodes qui agissent lors de l'entraînement. +Utiliser ces méthodes peut causer une augmentation de certain risque liée à la confidentialité des donnée d'entraînement, ainsi il est admis qu'il y ai un compromis à faire enre equitée et confidentialitée~\cite{dudu2023sok}. +Cependant ce compromis ne concerne que les risquées liée aux attaque de MIA et rentre en coflit avec la confidentialité diférentielles~\cite{chang2021privacy,cummings,ijcai2022p766}. + +Dans ce chapitre nous allons étudier les intéractions entre ces mécanismes d'équitée et l'attaque AIA. +Nous allons montrer que sous cet angle, l'équitée et la confidentialitée travailent de concert. +Cette étude peut être vue sous deux angles. +Le premier aspect consiste à étudier comment les mécanisme d'équitée peuvent être utilisé pour mitiger différent types d'AIA. +Le second aspect, en lien avec le primer, est d'utiliser les AIA pour contrôler dans un environement boîte noire le niveau d'équitée d'un modèle. + +\subsection{Contributions} +Dans ce chapitre nous apportons les contributions suivante : +\begin{itemize} + \item Une définition de l'équitée qui généralise la \textit{demographic parity} à la regression. + \item Diverse relations analytique et synthétques entre AIA, \textit{demographic parity} et \textit{equality of odds} qui remplissent les objectifs de: + calcul de niveau d'équitée en boîte noire et + garanties théoriques sur le niveau de confidentialité des donnée des utilisateurs de modèles. + \item La construction de deux nouvelles attaque AIA efficaces quand l'attribut sensible présente un déséquilibre. + \item Une étude empirique des relations entre niveau d'équitée, utilisation d'algorithmes imposants l'équitée et succès des attaques AIA. +\end{itemize} diff --git a/aia/main.tex b/aia/main.tex index 07f5b67..8e6059c 100644 --- a/aia/main.tex +++ b/aia/main.tex @@ -1,17 +1,24 @@ +\section{Introduction} +\input{aia/intro} -\section{AIA} -\section{Modèle de menace} -threat model -\label{sec:aia-tm} +\section{Equitée en regression} +\input{aia/fair_reg} -\section{Classification} -\input{aia/classif} +\section{Etude théorique de la relation entre AIA et équitée} +\input{aia/theo} \section{Regression} \label{sec:aia-soft} +\section{AIA} +\section{Modèle de menace} +threat model +\label{sec:aia-tm} \section{Méthodologie} \subsection{Jeux de donné} \label{sec:aia-methodo-jeu} The US census is a snapshot of the US adult population that is done every ten year by the US government\footnote{www.census.gov}. It produces a database where each row is an individual and each column is an attribute that describe people. + +\section{Résultats} +\input{aia/resultats} diff --git a/aia/resultats.tex b/aia/resultats.tex new file mode 100644 index 0000000..efe0060 --- /dev/null +++ b/aia/resultats.tex @@ -0,0 +1,119 @@ + +\begin{figure} + \centering + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/census/census_egd_attack_hard_race.pdf} + \caption{Census (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/census/census_egd_attack_hard_sex.pdf} + \caption{Census (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/compas/compas_egd_attack_hard_race.pdf} + \caption{Compas (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/compas/compas_egd_attack_hard_sex.pdf} + \caption{Compas (sex)} + \end{subfigure} +\centering + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/meps/meps_egd_attack_hard_race.pdf} + \caption{Meps (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/meps/meps_egd_attack_hard_sex.pdf} + \caption{Meps (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_attack_hard_race.pdf} + \caption{Lfw (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/egd/lfw/lfw_egd_attack_hard_sex.pdf} + \caption{Lfw (sex)} + \end{subfigure} + + \caption{For \AIAHard, we observe that EGD reduces the attack accuracy to random guess ($\sim$50\%)} + \label{fig:AdaptAIAEGD} +\end{figure} + + + +\begin{figure}[!htb] + \centering + \footnotesize + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_soft_experimental_race.pdf} + \caption{Census (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_soft_experimental_sex.pdf} + \caption{Census (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_race.pdf} + \caption{Compas (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_sex.pdf} + \caption{Compas (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_soft_experimental_race.pdf} + \caption{Meps (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_soft_experimental_sex.pdf} + \caption{Meps (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_race.pdf} + \caption{Lfw (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_sex.pdf} + \caption{Lfw (sex)} + \end{subfigure} + + \caption{For both \AIASoft and \AIAHard, Adversarial debisaing reduces the attack accuracy to random guess ($\sim$50\%). For \AIAHard, the theoretical bound on attack accuracy matches with the empirical results.} + \label{fig:AdaptAIADebias} +\end{figure} + +\begin{figure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_hard_race.pdf} + \caption{Census (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/census/census_advdeb_attack_hard_sex.pdf} + \caption{Census (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_hard_race.pdf} + \caption{Compas (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/compas/compas_advdeb_attack_hard_sex.pdf} + \caption{Compas (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_hard_race.pdf} + \caption{Meps (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/meps/meps_advdeb_attack_hard_sex.pdf} + \caption{Meps (sex)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_hard_race.pdf} + \caption{Lfw (race)} + \end{subfigure} + \begin{subfigure}{0.24\linewidth} + \includegraphics[width=\linewidth]{ACSAC/figures/advdebias/lfw/lfw_advdeb_attack_hard_sex.pdf} + \caption{Lfw (sex)} + \end{subfigure} + \caption{adverarial debiasing hard} + \label{fig:aia-adv-hard} +\end{figure} diff --git a/aia/theo.tex b/aia/theo.tex new file mode 100644 index 0000000..2a826b1 --- /dev/null +++ b/aia/theo.tex @@ -0,0 +1,89 @@ +\subsection{Utiliser l'équitée pour mitiger les AIA} +Commencons par présenter le résultat le plus générale, qui fonctionne aussi bien pour des modèle de classification que pour des regression. +Ce résultats est aussi indépendant du type d'attribut binaire, quantitatif au qualitatif. + +\begin{theorem} + \label{th:aia-dpgood} + Les deux propositions suivantes sont équivalantes : + \begin{enumerate} + \item Le modèle cible satisfait la démographic parity + \item Toutes les attaques utilisant la prédiction pour inférer l'attribut sensible sont des CCA. + \end{enumerate} + + Et aussi, les deux propositions suivantes sont équivalantes : + \begin{enumerate} + \item Le modèle cible satisfait la démographic parity généraliée + \item Toutes les attaques utilisants le logit pour inférer l'attribut sensible sont des CCA. + \end{enumerate} +\end{theorem} + +\begin{proof} + Par définition, la \textit{demographic parity} (respectivement généralisée) est equivalante à l'inpépendance entre l'attribut sensible et la prediction (respectivement le logit). + Ainsi, d'après le Lemme~\ref{lemme:aia-xycca} dire que tout classifieur de l'attribute sensible utilisant la prédiction (respectivement le logit) est un CCA est équivalant à dire que le modèle cible respecte la \textit{demographic parity} (respectivement généralisée). +\end{proof} + +Ce résultat nous apprend que s'assurer que le modèle cible satisfait la \textit{demographic parity} permet de s'assurer que les attribut sensible des utilisateur soient protégé lors de l'utilisation du modèle. +Dans le cas d'un modèle cible qui réalise une classifiction binaire et en considérant un attribut binaire nous avons une propriété plus précise. + +\begin{propriete} + Soit $(\Omega,\mathcal{T},P)$ un espace probabilisé et $(\{0,1\}$, $\mathcal{P}(\{0,1\}))$ des espaces mesurables. + Soit les variables aléatoires suivantes + \begin{itemize} + \item L'étiquette $Y:(\Omega,\mathcal{T})\rightarrow (\{0,1\},\mathcal{P}(\{0,1\})$ + \item La donnée d'entrée $X:(\Omega,\mathcal{T})\rightarrow (F,\mathcal{F})$ + \item L'attribute sensible $S:(\Omega,\mathcal{T})\rightarrow(\{0,1\},\mathcal{P}(\{0,1\}))$ + \item L'attaque $a:(\Omega,\mathcal{T})\rightarrow\mathcal{P}(\{0,1\}))$ + \item Le modèle cible $f:(\Omega,\mathcal{T})\rightarrow\mathcal{P}(\{0,1\}))$ + \end{itemize} + Alors nous avons + \begin{equation*} + \text{max}_{a}BA(a) = \frac{1}{2}(1+\text(DemParLvl(f))) + \end{equation*} +\end{propriete} + +\begin{proof} + On pause $\hat{Y}=f\circ X$. + L'ensemble $A$ des fonction de $\{0,1\}$ vers $\{0,1\}$ contient quatre éléments : +$a_0=0$, $a_1=id$, $a_2=1-id$ et $a,3=1$. + Pour chaque attaque $a\in A$ la \textit{balanced accuracy} de $a$ est +$BA(a) = \frac{1}{2}(P(a\circ \hat{Y}=0|S=0) + P(a\circ \hat{Y}=1|S=1))$. +Nous avons $BA(b_0) = BA(b_3) = \frac{1}{2}$ il n'est donc pas nécessaire de considérer ces éléments pour résoudre le problème d'optimisation. +Ce problème s'écrit $\text{max}_{a\in A}BA(a)) = \text{max}(BA(a_1), BA(a_2))$. +Nous remarquon que $a_1\circ \hat{Y}=\hat{Y}$ et $a_2\circ \hat{Y}=1 - \hat{Y}$. +Ainsi, +{ +\begin{align*} + BA(a_1) &= \frac{1}{2}(P(\hat{Y}=0|S=0) + P(\hat{Y}=1|S=1))\\ + &=\frac{1}{2}(1+P(\hat{Y}=1|S=1) - P(\hat{Y}=1|S=0)) +\end{align*} +} +et +{ +\begin{align*} + BA(a_2)=\frac{1}{2}(1+P(\hat{Y}=1|S=0) - P(\hat{Y}=1|S=1)) +\end{align*} +} +Donc, +{ +\begin{align*} + &\text{max}_{a\in B}BA(a) \\ + = &\frac{1}{2}\left(1+\text{max}\left( + \begin{matrix} + P(\hat{Y}=0|S=0) -P(\hat{Y}=1|S=1)\\ + P(\hat{Y}=1|S=0) -P(\hat{Y}=0|S=1) + \end{matrix} + \right)\right)\\ + =&\frac{1}{2}(1+|P(\hat{Y}=1|S=1) - P(\hat{Y}=1|S=0)|) +\end{align*} +} +\end{proof} + +Ainsi pour le classifieur binaire avec attribut sensbile binaire, il est suffisant de calculer le DemParLvl du modèle cible pour connaitre le maximum de \textit{balanced accuracy} ateignable par n'importe quelle attaque. +De plus, nous voyons que la \textit{balanced accuracy} maximial d'attaque vaut ${1}{2}$ si et seulement si $\text{DemParLvl}=0$. +C'est à dire que $f$ satisfait DemPar est équivalant à dire que tout attaque à une \textit{balanced accuracy} égale à $\frac{1}{2}$. + +Grâce au Théorème~\ref{th:aia-dpgood} nous savons aussi que tout autre définition d'équtiée qui n'implique pas la paritée démographique ne permet pas de mitiger les AIA. +Par exemple, nous allons montrer que l'égalitée de chances de la Définition~\ref{def:background-eq-eoo} en permet pas de mitiger l'AIA dans le cas binaire que nous avons étuié précédement. + +\subsection{Utiliser l'AIA pour contrôler le niveau d'équitée}. + diff --git a/background/eq.tex b/background/eq.tex index 8a76ee7..446ad95 100644 --- a/background/eq.tex +++ b/background/eq.tex @@ -1,22 +1,24 @@ \label{sec:bck_fair} -Algorithmic fairness aims at reducing biases in ML model predictions. -Indeed, data records belonging to certain subgroups influence $targetmodel$'s predictions more than others. -For instance in criminal justice, the ethnicity of a culprit plays a non-negligible role in the prediction of them reoffending~\cite{fairjustice}. Generally, data records in the minority subgroup face unfair prediction behaviour compared to data records in the majority subgroup. These subgroups are identified based on a sensitive attribute (e.g., race or sex). -Those biases are learnt by $targetmodel$ as they are part of the distribution of the training dataset. -There is two main categories of fairness of a ML model: +L'équitée algorithmique à pour but de réduire les bias dans le modèle prédictif. +En effet, le fait qu'une donnée d'entraînement appratienne à certainne minorité peut avoir un impacte sur la qualitée de la prédiction. +Par exemple en justice prédictie, la couleur de peau d'un peau d'un coupable jou un rôle qui n'est pas négligable dans la prédiction du récidivisme au Etats Unis~\cite{fairjustice}. +Les minoritée sont identifié par un attribut sensible comme la couleur de peau, le genre ou l'orientation sexuelle. +Pour savoir si un attribut est sensible ou non, nous pouvons nous référer à l'observatoire de inégalités. +Ces bias sont appris par le modèle car ils sont présent dans les donnés d'entraînement qui reflète la population dans laquelle ces donnée ont été prélevés. -\textbf{Individual fairness} ensures that two data records with same attributes except for $S$ have the same model prediction. -This notion does not dwell on sensitive attribute and as such is not really useful in our goal of mitigating attribute inference attack at inference time. -So we set it aside for the rest of the paper. +L'équitée en apprantissag automatique se présente sous deux aspect qui mettent lumière deux visions différentes : -\textbf{Group fairness} comes from the idea that different subgroups defined by an attribute such a skin color or gender should be treated equally. -We focus our study on group fairness where $S$ represents either sex or race (i.e., $S(i)$ equals to 0 for woman, 1 for man, and 0 for black, 1 for white, respectively). -There are different definitions of group fairness which have been introduced in prior work. -We discuss two well-established and commonly used metrics: demographic parity and equality of odds. +\textbf{L'équitée individuelle}\footnote{Individual fairness} +cherche à faire en sorte que deux donnée, à toutes choses égale exepté l'attribut sensible, produisent la même prédiction. + +\textbf{L'équitée de groupe}\footnote{Group fairness} +Vient de l'idée que different sous groupes défini par un critère de discrimination devrait être traite de manière similaire. +Il y a différentes définitions mathématiques de l'équite de groupe. +Nous allons en regarder deux qui sont bien établis dans la litérature et souvant utilisé : la paritée demographique\footnote{Demographic parity} et l'équitée de chances\footnote{Equality of odds}. \begin{definition} -\label{def:dp} +\label{def:background-eq-dp} $\hat{Y}$ satisfies demparity for $S$ if and only if: $P(\hat{Y}=0 | S=0) = P(\hat{Y}=0 | S=1)$. From that, we will call $|P(\hat{Y}=0 | S=0) - P(\hat{Y}=0 | S=1)|$ the demPar-level of $\hat{Y}$. \end{definition} @@ -28,7 +30,7 @@ However, this may result in different false positive and true positive rates if Hardt et al.~\cite{fairmetric2} proposed eo as a modification of demparity to ensure that both the true positive rate and false positive rate will be the same for each population. \begin{definition} - \label{def:eo} + \label{def:background-eq-eoo} $\hat{Y}$, classifier of $Y$, satisfies equality of odds for $S$ if and only if: $\forall (\hat{y},y)\in\{0,1\}^2 \quad P(\hat{Y}=\hat{y} | S=0,Y=y) = P(\hat{Y}=\hat{y} | S=1,Y=y)$. \end{definition} @@ -1,3 +1,10 @@ +######################"" +#Notes +@misc{dati2024declaration, + title={Déclaration de Mme Rachida Dati, ministre de la culture, lors de l'installation de la Commission d'enrichissement de la langue française, le 27 mai 2024.} + author={Dati, Rachida}, + year={2024} +} ######################################" #Background @BOOK{lecun2019quand, @@ -1336,3 +1343,1234 @@ series = {NIPS'14} year={2017}, organization={IEEE} } + +@misc{carlini2022membership, + title={Membership Inference Attacks From First Principles}, + author={Nicholas Carlini and Steve Chien and Milad Nasr and Shuang Song and Andreas Terzis and Florian Tramer}, + year={2022}, + eprint={2112.03570}, + archivePrefix={arXiv}, + primaryClass={cs.CR} +} + +@inproceedings{salem2023sok, + title={SoK: Let the privacy games begin! A unified treatment of data inference privacy in machine learning}, + author={Salem, Ahmed and Cherubin, Giovanni and Evans, David and K{\"o}pf, Boris and Paverd, Andrew and Suri, Anshuman and Tople, Shruti and Zanella-B{\'e}guelin, Santiago}, + booktitle={Security \& Privacy}, + pages={327--345}, + year={2023}, +} +% + organization={IEEE} + + +@inproceedings{ijcai2022p766, + title = {Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey}, + author = {Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu}, + booktitle = {International Joint Conference on + Artificial Intelligence}, + + pages = {5470--5477}, + year = {2022}, + month = {7}, + +} +%publisher = {International Joint Conferences on Artificial Intelligence Organization}, + editor = {Lud De Raedt}, +note = {Survey Track}, + doi = {10.24963/ijcai.2022/766}, + url = {https://doi.org/10.24963/ijcai.2022/766}, + + +@article{accfairtradeoff, +author = {Pinzon, Carlos and Palamidessi, Catuscia and Piantanida, Pablo and Valencia, Frank}, +year = {2023}, +month = {05}, +pages = {1-30}, +title = {On the incompatibility of accuracy and equal opportunity}, +journal = {Machine Learning}, +} +% +doi = {10.1007/s10994-023-06331-y} + +@article{rodolfa2021empirical, + title={Empirical observation of negligible fairness--accuracy trade-offs in machine learning for public policy}, + author={Rodolfa, Kit T and Lamba, Hemank and Ghani, Rayid}, + journal={Nature Machine Intelligence}, + volume={3}, + number={10}, + pages={896--904}, + year={2021}, +} +% + publisher={Nature Publishing Group UK London} + +@article{zhai2022understanding, + title={Understanding why generalized reweighting does not improve over ERM}, + author={Zhai, Runtian and Dan, Chen and Kolter, Zico and Ravikumar, Pradeep}, + booktitle={International Conference on Learning Representation}, + year={2023} +} + +@article{ +veldanda2022fairness, +title={Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss}, +author={Akshaj Kumar Veldanda and Ivan Brugere and Jiahao Chen and Sanghamitra Dutta and Alan Mishler and Siddharth Garg}, +journal={Transactions on Machine Learning Research}, +issn={2835-8856}, +year={2023}, + +} +%url={https://openreview.net/forum?id=f4VyYhkRvi}, +note={} + +% general +% url = {https://arxiv.org/abs/2206.10923}, +@misc{arxivmichael, + doi = {10.48550/ARXIV.2206.10923}, + author = {Maheshwari, Gaurav and Perrot, Michaël}, + title = {FairGrad: Fairness Aware Gradient Descent}, + publisher = {arXiv}, + year = {2022}, +} + + +@InProceedings{classIMb1, + title = {Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding}, + author = {Guo, Lan-Zhe and Li, Yu-Feng}, + booktitle = {International Conference on Machine Learning}, + pages = {8082--8094}, + year = {2022}, + editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, + volume = {162}, + month = {17--23 Jul}, + +} +% + pdf = {https://proceedings.mlr.press/v162/guo22e/guo22e.pdf}, + url = {https://proceedings.mlr.press/v162/guo22e.html} +series = {Proceedings of Machine Learning Research}, + publisher = {PMLR}, + +@article{classIMb2, + title={Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks}, + author={Sivaramakrishnan Rajaraman and Prasanth Ganesan and Sameer K. Antani}, + journal={PLoS ONE}, + year={2021}, + volume={17}, +} +% + url={https://api.semanticscholar.org/CorpusID:238259577} + +@misc{classIMb3, + author = {Jason Brownlee}, + title = {{A} {G}entle {I}ntroduction to {T}hreshold-{M}oving for {I}mbalanced {C}lassification - {M}achine{L}earning{M}astery.com}, + year = {}, + note = {[Accessed 31-08-2023]}, +} +%issn = {0022-0000}, +%url = {https://www.sciencedirect.com/science/article/pii/S002200009791504X}, +@article{saddlepointsolve, +title = {A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting}, +journal = {Journal of Computer and System Sciences}, +volume = {55}, +number = {1}, +pages = {119-139}, +year = {1997}, +author = {Yoav Freund and Robert E Schapire} +} +% +doi = {10.1006/jcss.1997.1504}, + + +%isbn = {1595933832}, +%address = {New York, NY, USA}, +@inproceedings{curves, +author = {Davis, Jesse and Goadrich, Mark}, +title = {The Relationship between Precision-Recall and ROC Curves}, +year = {2006}, +booktitle = {International Conference on Machine Learning}, +pages = {233–240}, + +} +% +publisher = {Association for Computing Machinery}, +doi = {10.1145/1143844.1143874}, +location = {Pittsburgh, Pennsylvania, USA}, +series = {ICML '06} + +@inproceedings{cormode, +author = {Cormode, Graham}, +title = {Personal Privacy vs Population Privacy: Learning to Attack Anonymization}, +year = {2011}, +booktitle = {International Conference on Knowledge Discovery and Data Mining}, +pages = {1253–1261}, +} +%doi = {10.1145/2020408.2020598}, +%location = {San Diego, California, USA}, +series = {KDD '11} +%publisher = {Association for Computing Machinery}, + +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%issn = {0360-0300}, +%url = {https://doi.org/10.1145/3457607}, +@article{surveyfair, +author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram}, +title = {A Survey on Bias and Fairness in Machine Learning}, +year = {2021}, +volume = {54}, +number = {6}, +journal = {Comput. Surv.}, +month = {jul}, +articleno = {115}, +numpages = {35}, +} +% +doi = {10.1145/3457607}, + +@article{attinfSocial1, +author = {Gong, Neil Zhenqiang and Talwalkar, Ameet and Mackey, Lester and Huang, Ling and Shin, Eui Chul Richard and Stefanov, Emil and Shi, Elaine (Runting) and Song, Dawn}, +title = {Joint Link Prediction and Attribute Inference Using a Social-Attribute Network}, +year = {2014}, +volume = {5}, +number = {2}, +journal = {Trans. Intell. Syst. Technol.}, +} +% +doi = {10.1145/2594455}, +publisher = {Association for Computing Machinery}, + +%address = {New York, NY, USA}, + +%issn = {2471-2566}, +%url = {https://doi.org/10.1145/3154793}, +%numpages = {30}, +%%month = {jan}, +@article{attinfSocial2, +author = {Gong, Neil Zhenqiang and Liu, Bin}, +title = {Attribute Inference Attacks in Online Social Networks}, +year = {2018}, +volume = {21}, +number = {1}, +journal = {Trans. Priv. Secur.}, +articleno = {3}, +} +% +doi = {10.1145/3154793}, +publisher = {Association for Computing Machinery}, + +%isbn = {978-1-931971-32-4}, +%address = {Austin, TX}, +%url = {https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/gong}, +%publisher = {USENIX Association}, +%month = aug, +@inproceedings {attinfSocial3, +author = {Neil Zhenqiang Gong and Bin Liu}, +title = {You Are Who You Know and How You Behave: Attribute Inference Attacks via Users{\textquoteright} Social Friends and Behaviors}, +booktitle = {USENIX Security Symposium }, +year = {2016}, +pages = {979--995}, +} + + + %URL = {https://hal.inria.fr/hal-00748162}, + %ADDRESS = {San Diego, United States}, + %MONTH = Feb, +@inproceedings{attinfSocial4, + TITLE = {{You Are What You Like! Information Leakage Through Users' Interests}}, + YEAR = {2012}, + AUTHOR = {Chaabane, Abdelberi and Acs, Gergely and Kaafar, Mohamed Ali}, + BOOKTITLE = {Network and Distributed System Security Symposium}, + PAGES = {1-14}, +} + + + + +@inproceedings{attinfSocial5, + author={Elena Zheleva and Lise Getoor}, + title={To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles}, + year={2009}, + BOOKTITLE = {International Conference on World Wide Web}, + pages={531-540}, + doi={10.1145/1526709.1526781}, +} + + + +%isbn = {9781450349130}, +%publisher = {International World Wide Web Conferences Steering Committee}, +%address = {Republic and Canton of Geneva, CHE}, +%url = {https://doi.org/10.1145/3038912.3052695}, +@inproceedings{attinfSocial6, +author = {Jia, Jinyuan and Wang, Binghui and Zhang, Le and Gong, Neil Zhenqiang}, +title = {AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields}, +year = {2017}, +booktitle = {International Conference on World Wide Web}, +pages = {1561–1569}, +location = {Perth, Australia}, +series = {WWW '17} +} +% +doi = {10.1145/3038912.3052695}, + + +%isbn = {9781450382878}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +@inbook{dysan, +author = {Boutet, Antoine and Frindel, Carole and Gambs, S\'{e}bastien and Jourdan, Th\'{e}o and Ngueveu, Rosin Claude}, +title = {DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks}, +year = {2021}, +doi = {10.1145/3433210.3453095}, +booktitle = {Asia Conference on Computer and Communications Security}, +pages = {672–686}, +} +%serie = {ASIA CCS '21} + +@inproceedings{attprivacy, +author = {Zhang, Wanrong and Ohrimenko, Olga and Cummings, Rachel}, +title = {Attribute Privacy: Framework and Mechanisms}, +year = {2022}, +isbn = {9781450393522}, +booktitle = {Fairness, Accountability, and Transparency}, +pages = {757–766}, +numpages = {10}, + +} +%url = {https://doi.org/10.1145/3531146.3533139}, +doi = {10.1145/3531146.3533139}, +keywords = {Pufferfish privacy, attribute privacy, formal privacy frameworks, privacy-preserving mechanisms}, +series = {FAccT '22} +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, + + +%differential privacy and fairness +@inproceedings{dispvuln, +author = {Mohammad Yaghini and Bogdan Kulynych and Carmela Troncoso}, +title = {Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning}, +year = {2022}, +booktitle = {Privacy Enhancing Technologies Symposium} +} + + +%isbn = {9781450391405}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +@inproceedings{GongMIAUnfair, +author = {Zhong, Da and Sun, Haipei and Xu, Jun and Gong, Neil and Wang, Wendy Hui}, +title = {Understanding Disparate Effects of Membership Inference Attacks and Their Countermeasures}, +year = {2022}, +booktitle = {Asia Conference on Computer and Communications Security}, +pages = {959–974}, +} +%location = {Nagasaki, Japan}, +series = {ASIA CCS '22} +doi = {10.1145/3488932.3501279}, + + +%sbn = {9781450311151}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/2090236.2090255}, +@inproceedings{indivfairness, +author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Richard}, +title = {Fairness through Awareness}, +year = {2012}, +booktitle = {Innovations in Theoretical Computer Science}, +pages = {214–226}, +} +%doi = {10.1145/2090236.2090255}, +location = {Cambridge, Massachusetts}, +series = {ITCS '12} + +@inproceedings{outIndist, +author = {Dwork, Cynthia and Kim, Michael P. and Reingold, Omer and Rothblum, Guy N. and Yona, Gal}, +title = {Outcome indistinguishability}, +year = {2021}, +booktitle = {Symposium on Theory of Computing}, +pages = {1095–1108}, +numpages = {14}, +} +%isbn = {9781450380539}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3406325.3451064}, +doi = {10.1145/3406325.3451064}, +keywords = {Prediction, Fairness, Computational Indistinguishability}, +location = {Virtual, Italy}, +series = {STOC 2021} + + + +%isbn = {9781450369367}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3351095.3372872}, +@inproceedings{dpfair, +author = {Pujol, David and McKenna, Ryan and Kuppam, Satya and Hay, Michael and Machanavajjhala, Ashwin and Miklau, Gerome}, +title = {Fair Decision Making Using Privacy-Protected Data}, +year = {2020}, +booktitle = {Fairness, Accountability, and Transparency}, +pages = {189–199}, +} +% +doi = {10.1145/3351095.3372872}, +location = {Barcelona, Spain}, +series = {FAT* '20} + +%url={https://ojs.aaai.org/index.php/AAAI/article/view/17193}, +%month={May}, +@article{fairprivatelagrangian, +title={Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach}, +volume={35}, +number={11}, +journal={AAAI Conference on Artificial Intelligence}, +author={Tran, Cuong and Fioretto, Ferdinando and Van Hentenryck, Pascal}, +year={2021}, +pages={9932-9939} +} + +%editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, + %series = {Proceedings of Machine Learning Research}, + %month = {09--15 Jun}, + %publisher = {PMLR}, + %pdf = {http://proceedings.mlr.press/v97/jagielski19a/jagielski19a.pdf}, + %url = {https://proceedings.mlr.press/v97/jagielski19a.html} +@InProceedings{dpfairlearn, + title = {Differentially Private Fair Learning}, + author = {Jagielski, Matthew and Kearns, Michael and Mao, Jieming and Oprea, Alina and Roth, Aaron and -Malvajerdi, Saeed Sharifi and Ullman, Jonathan}, + booktitle = {International Conference on Machine Learning}, + pages = {3000--3008}, + year = {2019}, + volume = {97}, +} + +@incollection{dpaccdisp, +title = {Differential Privacy Has Disparate Impact on Model Accuracy}, +author = {Bagdasaryan, Eugene and Poursaeed, Omid and Shmatikov, Vitaly}, +booktitle = {Advances in Neural Information Processing Systems}, +pages = {15479--15488}, +year = {2019}} + +%isbn = {978-1-939133-06-9}, +%address = {Santa Clara, CA}, +%url = {https://www.usenix.org/conference/usenixsecurity19/presentation/jayaraman}, +%publisher = {USENIX Association}, +%month = aug, +@inproceedings {dpVacc, +author = {Bargav Jayaraman and David Evans}, +title = {Evaluating Differentially Private Machine Learning in Practice}, +booktitle = {USENIX Security Symposium}, +year = {2019}, +pages = {1895--1912}, +} + +%isbn = {9781450367110}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3314183.3323847}, +@inproceedings{cummings, +author = {Cummings, Rachel and Gupta, Varun and Kimpara, Dhamma and Morgenstern, Jamie}, +title = {On the Compatibility of Privacy and Fairness}, +year = {2019}, +booktitle = {Conference on User Modeling, Adaptation and Personalization}, +pages = {309–315}, + +} +%doi = {10.1145/3314183.3323847}, +series = {UMAP'19 Adjunct} +location = {Larnaca, Cyprus}, + +@techreport{ec2019ethics, + address = {Brussels}, + author = {{High-Level Expert Group on AI}}, + institution = {European Commission}, + language = {eng}, + month = apr, + title = {Ethics guidelines for trustworthy AI}, + type = {Report}, + year = {2019} +} +% + url = {https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai}, + +@inproceedings{nist, + title={A Taxonomy and Terminology of Adversarial Machine Learning}, + author={Elham Tabassi and Kevin J. Burns and M. Hadjimichael and Andres Molina-Markham and Julian Sexton}, + year={2019}, + booktitle = {NIST Interagency/Internal Report} +} +% + url = {https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf}, + +@inproceedings{dpia, +title={Art. 35 {GDPR} Data protection impact assessment}, +url={https://gdpr-info.eu/art-35-gdpr/}, +author={European Union Law}, +year={2018}, +booktitle={General Data Protection Regulation (GDPR)} } + +@article{ico, +title={{AI} auditing and impact assessment: according to the UK information commissioner’s office}, +journal={AI and Ethics}, +author={Kazim, Emre and Denny, Danielle Mendes Thame and Koshiyama, Adriano}, +year={2021}, +month={Feb} } +%ISSN={2730-5953, 2730-5961}, url={http://link.springer.com/10.1007/s43681-021-00039-2}, DOI={10.1007/s43681-021-00039-2}, + +@inproceedings{whitehouse, +title={Guidance for Regulation of Artificial Intelligence Applications}, +author={White House}, +year = {2020}, +booktitle={Memorandum For The Heads Of Executive Departments And Agencies} } +%url={https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf}, +%metrics + +@INPROCEEDINGS{memprivNattpriv, + author={Zhao, Benjamin Zi Hao and Agrawal, Aviral and Coburn, Catisha and Asghar, Hassan Jameel and Bhaskar, Raghav and Kaafar, Mohamed Ali and Webb, Darren and Dickinson, Peter}, + booktitle={European Security \& Privacy}, + title={On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models}, + year={2021}, + pages={232-251}, + doi={10.1109/EuroSP51992.2021.00025} +} + +@article{duddu2023sok, + title={SoK: Unintended Interactions among Machine Learning Defenses and Risks}, + author={Duddu, Vasisht and Szyller, Sebastian and Asokan, N}, + journal={arXiv preprint arXiv:2312.04542}, + year={2023} +} + + +@inproceedings{suri2023dissecting, + title={Dissecting distribution inference}, + author={Suri, Anshuman and Lu, Yifu and Chen, Yanjin and Evans, David}, + booktitle={Conference on Secure and Trustworthy Machine Learning}, + pages={150--164}, + year={2023}, +} +% + organization={IEEE} + + +@article{de2020overview, + title={An overview of privacy in machine learning}, + author={De Cristofaro, Emiliano}, + journal={arXiv preprint arXiv:2005.08679}, + year={2020} +} + + +@article{pate2021fairness, + title={A Fairness Analysis on Private Aggregation of Teacher Ensembles}, + author={Tran, Cuong and Dinh, My H and Beiter, Kyle and Fioretto, Ferdinando}, + journal={arXiv preprint arXiv:2109.08630}, + year={2021} +} + +@article{fioretto2022differential, + title={Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey}, + author={Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu}, + journal={arXiv preprint arXiv:2202.08187}, + year={2022} +} + + +% attribute inference attacks in ML +%publisher = "Institute of Electrical and Electronics Engineers (IEEE)", +%address = "United States", +@inproceedings{zhao2021infeasibility, +title = "On the (in)feasibility of attribute inference attacks on machine learning models", +author = "Zhao, {Benjamin Zi Hao} and Aviral Agrawal and Catisha Coburn and Asghar, {Hassan Jameel} and Raghav Bhaskar and Kaafar, {Mohamed Ali} and Darren Webb and Peter Dickinson", +year = "2021", +pages = "232--251", +booktitle = "European Security \& Privacy", +} +% +doi = "10.1109/EuroSP51992.2021.00025", +serie = {EuroS&P '2021}, + +%isbn = {978-1-939133-31-1}, +%address = {Boston, MA}, +%url = {https://www.usenix.org/conference/usenixsecurity22/presentation/mehnaz}, +%publisher = {USENIX Association}, +%month = aug, +@inproceedings{MehnazAttInf, +author = {Shagufta Mehnaz and Sayanton V. Dibbo and Ehsanul Kabir and Ninghui Li and Elisa Bertino}, +title = {Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models}, +booktitle = {USENIX Security Symposium}, +year = {2022}, +pages = {4579--4596}, +} + +%isbn = {9781450338325}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%%url = {https://doi.org/10.1145/2810103.2813677}, +@inproceedings{fredrikson1, +author = {Fredrikson, Matt and Jha, Somesh and Ristenpart, Thomas}, +title = {Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures}, +year = {2015}, +booktitle = {Conference on Computer and Communications Security}, +pages = {1322–1333}, + +} +% +doi = {10.1145/2810103.2813677}, +location = {Denver, Colorado, USA}, +series = {CCS '15} + + +%isbn = {9781931971157}, +@inproceedings{fredrikson2, +author = {Fredrikson, Matthew and Lantz, Eric and Jha, Somesh and Lin, Simon and Page, David and Ristenpart, Thomas}, +title = {Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing}, +year = {2014}, +booktitle = {USENIX Conference on Security Symposium}, +pages = {17–32}, +} +% +location = {San Diego, CA}, +series = {SEC'14} + +@inproceedings{Song2020Overlearning, +title={Overlearning Reveals Sensitive Attributes}, +author={Congzheng Song and Vitaly Shmatikov}, +booktitle={International Conference on Learning Representations}, +year={2020} +} + + +%isbn = {9781450384544}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3460120.3484533}, +@inproceedings{malekzadeh2021honestbutcurious, +author = {Malekzadeh, Mohammad and Borovykh, Anastasia and G\"{u}nd\"{u}z, Deniz}, +title = {Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs}, +year = {2021}, +booktitle = {Conference on Computer and Communications Security}, +pages = {825–844}, +} +%location = {Virtual Event, Republic of Korea}, +series = {CCS '21} +doi = {10.1145/3460120.3484533}, + +@article{jayaraman2022attribute, + title={Are Attribute Inference Attacks Just Imputation?}, + author={Jayaraman, Bargav and Evans, David}, + journal={arXiv preprint arXiv:2209.01292}, + year={2022} +} + + +@inproceedings{yeom, + author={Yeom, Samuel and Giacomelli, Irene and Fredrikson, Matt and Jha, Somesh}, + booktitle={Computer Security Foundations Symposium}, + title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting}, + year={2018}, + pages={268-282}, +} +% + doi={10.1109/CSF.2018.00027} + +@inproceedings{Mahajan2020DoesLS, + title={Does Learning Stable Features Provide Privacy Benefits for Machine Learning Models?}, + author={Divyat Mahajan, Shruti Tople, Amit Sharma}, + booktitle = {NeurIPS PPML Workshop}, + year={2020} +} + +@inproceedings{Malekzadeh_2021, + + year = 2021, month = {nov}, + author = {Mohammad Malekzadeh and Anastasia Borovykh and Deniz Gündüz}, + title = {Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers{\textquotesingle} Outputs}, + booktitle = {Conference on Computer and Communications Security}} +% + publisher = {{ACM}}, +doi = {10.1145/3460120.3484533}, + url = {https://doi.org/10.1145%2F3460120.3484533}, + + +@INPROCEEDINGS{meminf, + author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly}, + booktitle={Security \& Privacy}, + title={Membership Inference Attacks Against Machine Learning Models}, + year={2017}, + pages={3-18},} +% + doi={10.1109/SP.2017.41} + +@article{chang2021privacy, + title={On the Privacy Risks of Algorithmic Fairness}, + author={Hongyang Chang and R. Shokri}, + journal={European Security \& Privacy}, + year={2021}, + pages={292-303} +} + +@article{duddu2022inferring, + title={Inferring Sensitive Attributes from Model Explanations}, + author={Duddu, Vasisht and Boutet, Antoine}, + journal={arXiv preprint arXiv:2208.09967}, + year={2022} +} + +%editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin}, + %publisher = {Curran Associates, Inc.}, + %url = {https://proceedings.neurips.cc/paper/2020/file/6b8b8e3bd6ad94b985c1b1f1b7a94cb2-Paper.pdf}, +@inproceedings{NEURIPS2020_6b8b8e3b, + author = {Zhao, Han and Chi, Jianfeng and Tian, Yuan and Gordon, Geoffrey J}, + booktitle = {Advances in Neural Information Processing Systems}, + pages = {9485--9496}, + title = {Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation}, + volume = {33}, + year = {2020} +} + + + +@ARTICLE{8515092, +author={S. A. {Osia} and A. {Taheri} and A. S. {Shamsabadi} and K. {Katevas} and H. {Haddadi} and H. R. {Rabiee}}, +journal={Transactions on Knowledge and Data Engineering}, +title={Deep Private-Feature Extraction}, +year={2020}, +volume={32}, +number={1}, +pages={54-66}, +} + + + +%eprint = {1707.00075} +@article{advfair, + author = {Alex Beutel and Jilin Chen and Zhe Zhao and Ed H. Chi}, + title = {Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations}, + year = {2017}, + publisher = {arXiv}, + doi = {10.48550/ARXIV.1707.00075}, +} + +%property inference attack + +@article{propinf, + title={Dataset-Level Attribute Leakage in Collaborative Learning}, + author={Zhang, Wanrong and Tople, Shruti and Ohrimenko, Olga}, + journal={arXiv:2006.07267}, + year={2020} +} + +%month = sep, +@article{propinf2, +author = {Ateniese, Giuseppe and Mancini, Luigi V. and Spognardi, Angelo and Villani, Antonio and Vitali, Domenico and Felici, Giovanni}, +title = {Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers}, +year = {2015}, +volume = {10}, +number = {3}, +journal = {Int. J. Secur. Netw.}, +pages = {137–150} +} + + +%isbn = {9781450356930}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3243734.3243834}, +@inproceedings{propinf3, +author = {Ganju, Karan and Wang, Qi and Yang, Wei and Gunter, Carl A. and Borisov, Nikita}, +title = {Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations}, +year = {2018}, +booktitle = {Conference on Computer and Communications Security}, +pages = {619–633}, + +} +%location = {Toronto, Canada}, +series = {CCS '18} +doi = {10.1145/3243734.3243834}, + + +@article{propinf4, + title={Formalizing and Estimating Distribution Inference Risks}, + author={Suri, Anshuman and Evans, David}, + journal={Privacy Enhancing Technologies}, + year={2022} +} + +@inproceedings{fedinference, +author={L. {Melis} and C. {Song} and E. {De Cristofaro} and V. {Shmatikov}}, +booktitle={Security \& Privacy}, +title={Exploiting Unintended Feature Leakage in Collaborative Learning}, +year={2019}, +pages={691-706} +} + +@INPROCEEDINGS {ferryExploit, +author = {J. Ferry and U. Aivodji and S. Gambs and M. Huguet and M. Siala}, +booktitle = {Conference on Secure and Trustworthy Machine Learning}, +title = {Exploiting Fairness to Enhance Sensitive Attributes Reconstruction}, +year = {2023}, +volume = {}, +issn = {}, +pages = {18-41}, +month = {feb} +} +%keywords = {training;measurement;learning systems;privacy;pipelines;training data;machine learning}, +doi = {10.1109/SaTML54575.2023.00012}, +url = {https://doi.ieeecomputersociety.org/10.1109/SaTML54575.2023.00012}, +publisher = {IEEE Computer Society}, +address = {Los Alamitos, CA, USA}, + + + +% defences against attribute inference attacks + +@inproceedings{10.5555/3042817.3042973, +author = {Zemel, Richard and Wu, Yu and Swersky, Kevin and Pitassi, Toniann and Dwork, Cynthia}, +title = {Learning Fair Representations}, +year = {2013}, +booktitle = {International Conference on Machine Learning}, +} +%serie = {ICML '13}, + +%month = jan, +@article{10.5555/3122009.3208010, +author = {Hamm, Jihun}, +title = {Minimax Filter: Learning to Preserve Privacy from Inference Attacks}, +year = {2017}, +volume = {18}, +number = {1}, +journal = {J. Mach. Learn. Res.}, +pages = {4704–4734} +} + +@inproceedings{10.5555/3327546.3327583, +author = {Moyer, Daniel and Gao, Shuyang and Brekelmans, Rob and Steeg, Greg Ver and Galstyan, Aram}, +title = {Invariant Representations without Adversarial Training}, +year = {2018}, +booktitle = {Advances in Neural Information Processing Systems} +} + +@inproceedings{10.5555/3294771.3294827, +author = {Xie, Qizhe and Dai, Zihang and Du, Yulun and Hovy, Eduard and Neubig, Graham}, +title = {Controllable Invariance through Adversarial Feature Learning}, +year = {2017}, +booktitle = {Advances in Neural Information Processing Systems} +} + +@InProceedings{pmlr-v80-madras18a, + title = {Learning Adversarially Fair and Transferable Representations}, + author = {Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard}, + pages = {3384--3393}, + year = {2018}, + volume = {80}, + booktitle = {Proceedings of Machine Learning Research}, +} + +@inproceedings{censoringadv, +title = "Censoring Representations with an Adversary", +author = "Harrison Edwards and Amos Storkey", +year = "2016", +booktitle = {International Conference on Learning Representations} +} +@inproceedings{NIPS2017_48ab2f9b, + author = {Louppe, Gilles and Kagan, Michael and Cranmer, Kyle}, + booktitle = {Advances in Neural Information Processing Systems}, + editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}, + pages = {}, + publisher = {Curran Associates, Inc.}, + title = {Learning to Pivot with Adversarial Networks}, + volume = {30}, + year = {2017} +} +%url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/48ab2f9b45957ab574cf005eb8a76760-Paper.pdf}, + +%isbn = {9781450360128}, +%publisher = {Association for Computing Machinery}, +%address = {New York, NY, USA}, +%url = {https://doi.org/10.1145/3278721.3278779}, +@inproceedings{debiase, +author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret}, +title = {Mitigating Unwanted Biases with Adversarial Learning}, +year = {2018}, +booktitle = {Conference on AI, Ethics, and Society}, +pages = {335–340}, +} +% location = {New Orleans, LA, USA}, +series = {AIES '18} +doi = {10.1145/3278721.3278779}, + + %month = {10}, +%pages = {}, +@article{preprocessing, +author = {Kamiran, Faisal and Calders, Toon}, +year = {2011}, +title = {Data Pre-Processing Techniques for Classification without Discrimination}, +volume = {33}, +journal = {Knowledge and Information Systems}, +} +%doi = {10.1007/s10115-011-0463-8} + +%series = {Proceedings of Machine Learning Research}, + %month = {10--15 Jul}, + %publisher = {PMLR}, + %pdf = {http://proceedings.mlr.press/v80/agarwal18a/agarwal18a.pdf}, + %url = {https://proceedings.mlr.press/v80/agarwal18a.html}, +@InProceedings{reductions, + title = {A Reductions Approach to Fair Classification}, + author = {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna}, + booktitle = {International Conference on Machine Learning}, + pages = {60--69}, + year = {2018}, + volume = {80}, +} + +@article{kifer2014pufferfish, +author = {Kifer, Daniel and Machanavajjhala, Ashwin}, +title = {Pufferfish: A framework for mathematical privacy definitions}, +year = {2014}, +issue_date = {January 2014}, +volume = {39}, +number = {1}, +issn = {0362-5915}, +journal = {Trans. Database Syst.}, +month = {jan}, +articleno = {3}, +numpages = {36}, +keywords = {Privacy, differential privacy} +} +%url = {https://doi.org/10.1145/2514689}, +doi = {10.1145/2514689}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, + +@inproceedings{song2017pufferfish, + title={Pufferfish privacy mechanisms for correlated data}, + author={Song, Shuang and Wang, Yizhen and Chaudhuri, Kamalika}, + booktitle={International Conference on Management of Data}, + pages={1291--1306}, + year={2017} +} + +@article{grinsztajn2022tree, + title={Why do tree-based models still outperform deep learning on typical tabular data?}, + author={Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l}, + journal={Advances in neural information processing systems}, + volume={35}, + pages={507--520}, + year={2022} +} + + + +@inproceedings {attriguard, +author = {Jinyuan Jia and Neil Zhenqiang Gong}, +title = {AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning}, +booktitle = {USENIX Security}, +year = {2018}, +pages = {513--529}, +} + + +% fairness metrics + +@article{fairmetric, +author = {Muhammad Bilal Zafar and Isabel Valera and Manuel Gomez-Rodriguez and Krishna P. Gummadi}, +title = {Fairness Constraints: A Flexible Approach for Fair Classification}, +journal = {Journal of Machine Learning Research}, +year = {2019}, +volume = {20}, +number = {75}, +pages = {1-42} +} + +@inproceedings{fairmetric2, +author = {Hardt, Moritz and Price, Eric and Srebro, Nathan}, +title = {Equality of Opportunity in Supervised Learning}, +year = {2016}, +booktitle = {Advances in Neural Information Processing Systems}, +pages = {3323–3331} +} + +@article{fairjustice, +author = {Alikhademi, Kiana and Drobina, Emma and Prioleau, Diandra and Richardson, Brianna and Purves, Duncan and Gilbert, Juan E.}, +title = {A Review of Predictive Policing from the Perspective of Fairness}, +year = {2022}, +issue_date = {Mar 2022}, +publisher = {Kluwer Academic Publishers}, +address = {USA}, +volume = {30}, +number = {1}, +issn = {0924-8463}, +journal = {Artif. Intell. Law}, +month = {mar}, +pages = {1–17}, +numpages = {17}, +keywords = {Predictive policing, Algorithmic fairness, Fairness, AI in criminal justice} +} +%url = {https://doi.org/10.1007/s10506-021-09286-4}, +doi = {10.1007/s10506-021-09286-4}, + +@article{folk, + title={Retiring Adult: New Datasets for Fair Machine Learning}, + author={Ding, Frances and Hardt, Moritz and Miller, John and Schmidt, Ludwig}, + journal={Advances in Neural Information Processing Systems}, + volume={34}, + year={2021} +} + +@inproceedings{ + SDV, + title={The Synthetic data vault}, + author={Patki, Neha and Wedge, Roy and Veeramachaneni, Kalyan}, + booktitle={International Conference on Data Science and Advanced Analytics}, + year={2016}, + pages={399-410}, + month={Oct} +} +% + doi={10.1109/DSAA.2016.49}, + +%misc{dpbad, +% + + author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Rich}, + + title = {Fairness Through Awareness}, + + eprint={1104.3913}, + archivePrefix={arXiv}, + year = {2011}, + primaryClass={cs.CY} + + +} +%keywords = {Computational Complexity (cs.CC), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences}, +%doi = {10.48550/ARXIV.1104.3913}, + url = {https://arxiv.org/abs/1104.3913}, +copyright = {arXiv.org perpetual, non-exclusive license} + +@INPROCEEDINGS{fairlog, + + author={Radovanović, Sandro and Petrović, Andrija and Delibašić, Boris and Suknović, Milija}, + + booktitle={International Conference on INnovations in Intelligent SysTems and Applications}, + + title={Enforcing fairness in logistic regression algorithm}, + + year={2020}, + + volume={}, + + number={}, + + pages={1-7}, +} +%doi={10.1109/INISTA49547.2020.9194676} + + +@misc{fairreg, + title={Fair Regression: Quantitative Definitions and Reduction-based Algorithms}, + author={Alekh Agarwal and Miroslav Dudík and Zhiwei Steven Wu}, + year={2019}, + eprint={1905.12843}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + + + +@InProceedings{fairaudit1, + title = {Blind Justice: Fairness with Encrypted Sensitive Attributes}, + author = {Kilbertus, Niki and Gascon, Adria and Kusner, Matt and Veale, Michael and Gummadi, Krishna and Weller, Adrian}, + booktitle = {International Conference on Machine Learning}, + pages = {2630--2639}, + year = {2018}, + editor = {Dy, Jennifer and Krause, Andreas}, + volume = {80}, + + month = {10--15 Jul}, + publisher = {PMLR}, + +} +%series = {Proceedings of Machine Learning Research}, +pdf = {http://proceedings.mlr.press/v80/kilbertus18a/kilbertus18a.pdf}, + url = {https://proceedings.mlr.press/v80/kilbertus18a.html}, + + + +@inproceedings{fairaudit2, +author = {Park, Saerom and Kim, Seongmin and Lim, Yeon-sup}, +title = {Fairness Audit of Machine Learning Models with Confidential Computing}, +year = {2022}, +isbn = {9781450390965}, +booktitle = {Web Conference 2022}, +pages = {3488–3499}, +numpages = {12}, +keywords = {Confidential computing, Algorithmic audit, Security and privacy, Fairness}, +} +%publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3485447.3512244}, +doi = {10.1145/3485447.3512244}, +location = {Virtual Event, Lyon, France}, +series = {WWW '22} + +@inproceedings{fairaudit3, +author = {Segal, Shahar and Adi, Yossi and Pinkas, Benny and Baum, Carsten and Ganesh, Chaya and Keshet, Joseph}, +title = {Fairness in the Eyes of the Data: Certifying Machine-Learning Models}, +year = {2021}, +booktitle = {Conference on AI, Ethics, and Society}, +pages = {926–935}, +numpages = {10}, +keywords = {machine-learning, cryptography, privacy, fairness}, +} +%publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3461702.3462554}, +doi = {10.1145/3461702.3462554}, +location = {Virtual Event, USA}, +series = {AIES '21} +isbn = {9781450384735}, + +@article{yadav2024fairproof, + title={FairProof: Confidential and Certifiable Fairness for Neural Networks}, + author={Yadav, Chhavi and Chowdhury, Amrita Roy and Boneh, Dan and Chaudhuri, Kamalika}, + journal={arXiv preprint arXiv:2402.12572}, + year={2024} +} + +@inproceedings{khedr2023certifair, + title={Certifair: A framework for certified global fairness of neural networks}, + author={Khedr, Haitham and Shoukry, Yasser}, + booktitle={AAAI Conference on Artificial Intelligence}, + volume={37}, + number={7}, + pages={8237--8245}, + year={2023} +} + +@article{urban20, +author = {Urban, Caterina and Christakis, Maria and W\"{u}stholz, Valentin and Zhang, Fuyuan}, +title = {Perfectly parallel fairness certification of neural networks}, +year = {2020}, +issue_date = {November 2020}, +volume = {4}, +number = {OOPSLA}, +journal = {Program. Lang.}, +month = {nov}, +articleno = {185}, +numpages = {30}, +keywords = {Static Analysis, Neural Networks, Fairness, Abstract Interpretation} +} +%publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, + +@inproceedings{ +chugg2023auditing, +title={Auditing Fairness by Betting}, +author={Ben Chugg and Santiago Cortes-Gomez and Bryan Wilder and Aaditya Ramdas}, +booktitle={Conference on Neural Information Processing Systems}, +year={2023}, +} +% +url={https://openreview.net/forum?id=EEVpt3dJQj} + +@inproceedings{yan2022active, + title={Active fairness auditing}, + author={Yan, Tom and Zhang, Chicheng}, + booktitle={International Conference on Machine Learning}, + pages={24929--24962}, + year={2022}, + organization={PMLR} +} + +@article{de2024fairness, + title={Fairness Auditing with Multi-Agent Collaboration}, + author={de Vos, Martijn and Dhasade, Akash and Bourr{\'e}e, Jade Garcia and Kermarrec, Anne-Marie and Merrer, Erwan Le and Rottembourg, Benoit and Tredan, Gilles}, + journal={arXiv preprint arXiv:2402.08522}, + year={2024} +} + +@inproceedings{ghosh2022algorithmic, + title={Algorithmic fairness verification with graphical models}, + author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S}, + booktitle={AAAI Conference on Artificial Intelligence}, + volume={36}, + number={9}, + pages={9539--9548}, + year={2022} +} + +@inproceedings{ghosh2023biased, + title={“How Biased are Your Features?”: Computing Fairness Influence Functions with Global Sensitivity Analysis}, + author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S}, + booktitle={Fairness, Accountability, and Transparency}, + pages={138--148}, + year={2023} +} + +@article{FairSquare, +author = {Albarghouthi, Aws and D'Antoni, Loris and Drews, Samuel and Nori, Aditya V.}, +title = {FairSquare: probabilistic verification of program fairness}, +year = {2017}, +issue_date = {October 2017}, +volume = {1}, +number = {OOPSLA}, +journal = {Program. Lang.}, +month = {oct}, +articleno = {80}, +numpages = {30}, +keywords = {Algorithmic Fairness, Probabilistic Inference, Probabilistic Programming} +} +%publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3133904}, +doi = {10.1145/3133904}, + +@article{saleiro2018aequitas, + title={Aequitas: A bias and fairness audit toolkit}, + author={Saleiro, Pedro and Kuester, Benedict and Hinkson, Loren and London, Jesse and Stevens, Abby and Anisfeld, Ari and Rodolfa, Kit T and Ghani, Rayid}, + journal={arXiv preprint arXiv:1811.05577}, + year={2018} +} + +@article{bastani2019probabilistic, + title={Probabilistic verification of fairness properties via concentration}, + author={Bastani, Osbert and Zhang, Xin and Solar-Lezama, Armando}, + journal={Programming Languages}, + volume={3}, + number={OOPSLA}, + pages={1--27}, + year={2019}, +} +% + publisher={ACM New York, NY, USA} + +@article{adler2018auditing, + title={Auditing black-box models for indirect influence}, + author={Adler, Philip and Falk, Casey and Friedler, Sorelle A and Nix, Tionney and Rybeck, Gabriel and Scheidegger, Carlos and Smith, Brandon and Venkatasubramanian, Suresh}, + journal={Knowledge and Information Systems}, + volume={54}, + pages={95--122}, + year={2018}, +} +% publisher={Springer} + +@inproceedings{black2020fliptest, + title={Fliptest: fairness testing via optimal transport}, + author={Black, Emily and Yeom, Samuel and Fredrikson, Matt}, + booktitle={Fairness, Accountability, and Transparency}, + pages={111--121}, + year={2020} +} + +@article{Justicia, +title={Justicia: A Stochastic SAT Approach to Formally Verify Fairness}, +volume={35}, +number={9}, journal={Conference on Artificial Intelligence}, author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.}, year={2021}, month={May}, pages={7554-7563} } +%url={https://ojs.aaai.org/index.php/AAAI/article/view/16925}, DOI={10.1609/aaai.v35i9.16925}, diff --git a/notations.tex b/notations.tex new file mode 100644 index 0000000..90b70aa --- /dev/null +++ b/notations.tex @@ -0,0 +1,14 @@ +Toutes les notations utilisés sont définies le première fois quelle sont introduites. +Pour faciliter la lecture nous fournissons ici un liste des notations avec la referecence de leur définitions. + +\begin{table} +\centering +\begin{tabular}{|c|c|c|} + \hline + \textbf{Symbole}&\textbf{Description}&\textbf{Définition}\\ + \hline + $f^{1}$&Fonction inverse ou image reciproque&\\ + \hline +\end{tabular} +\caption{Liste de notations} +\end{table} diff --git a/notes.tex b/notes.tex new file mode 100644 index 0000000..3d7d3c5 --- /dev/null +++ b/notes.tex @@ -0,0 +1,6 @@ +Nous avons souaité rédiger ce manuscrit en français pour plusieurs raison qu'il nous semble important de mettre en avant. +Le sujet premier de ce manuscrit, l'intelligence artificielle est un sujet majeur de souveraineté nationale~\cite{villani2018donner}. +Cette souevraineté est intimement lié à la francophonie car comme l'explique Rachida Dati, ministre de la culture, +\textquote{la langue doit vivre au même rythme pour restituer la création, l'invention, l'innovation, pour nous permettre de penser et d'exprimer toutes les réalités du monde contemporain. Et pour rester une grande langue internationale, il faut pouvoir tout dire, tout nommer, tout traduire}~\cite{dati2024declaration}. +Ainsi nous nous somme efforcé de traduire les termes techniques de l'apprentissage automatique qui viennet tous de l'anglais. +Pour éviter que notre traduction soit trop confuse pour les lecteurs habitué aux terme anglais, pour chaque terme traduit nous indiquons son originie anglaise en note de bas de page à se premiètre occurence. diff --git a/remerciements.tex b/remerciements.tex index 225a1a0..0e869c4 100644 --- a/remerciements.tex +++ b/remerciements.tex @@ -1,24 +1,40 @@ -Merci à ma épouse, Emeline, pour son soutien, ses conseils, ses rélectures de mon orthopgraphe et pour m'avoir aidé avec le Théorème~\ref{th:fini-em}. +Merci à mon épouse, Emeline, pour son soutien, ses conseils, ses rélectures de mon orthopgraphe et pour m'avoir aidé avec le Théorème~\ref{th:fini-em}. Merci à Antoine Boutet et Mathieu Cunche pour leur encadrement. -Merci les copains Samuel, Thomas, Léo, Bastien, Rémi, Adrien, Nathan, Amine, Abhi. +Merci à ma mère Joëlle et mon père Pieter pour ne m'avoir jamais laché. Merci à toute l'équipe Privatics de l'INRIA pour les super séminaires toujour passionants. Merci à Frederic LeMouel, à Linda Soumari, et à tout le laboratoir CITI de l'INSA Lyon pour leur accompagnement. -Merci à Sébastien Gambs pour son acceuil chaleureux à l'Université de Québec à Montréal. - Merci à Cécile Mercadier et Clément Marteau d'avoir cru en moi. -Merci à Mr.Noyer, Romain Bondil, Ludovic Menneteau, Bijan Mohammadi et Andro Mikelic ainsi que tous les enseignants qui ont su me montrer la beauté des mathématiques. +Merci à Sébastien Gambs pour son accueil chaleureux à l'Université de Québec à Montréal. -Merci à ma mère Joëlle et mon père Pieter pour ne m'avoir jamais laché. +Merci à Mr.Noyer, Romain Bondil, Ludovic Menneteau, Bijan Mohammadi et Andro Mikelic ainsi que tous les enseignants qui ont su me montrer la beauté des mathématiques. Merci à ma petite soeur Claire pour avoir activement contribué à faire de moi qui je suis. -Merci à Maryse et Jean-Claude pour leur bienveillance. +Merci les copains : +Abhi, +Adrien, +Amine, +Anthonin, +Bastien, +Benoit, +Benoît, +Celestin, +Léo, +Nathan, +Rémi, +Samuel, +Thomas, +Virgile. + +Merci à Maryse, Jean-Claude et Patricia pour leur bienveillance. + +Merci à tous les habitants de Saint-Jean-De-Cuculles pour leur accueil. Merci à Raclette et Cookie pour leur mignonerie. diff --git a/template_these_INSA_cotut.tex b/template_these_INSA_cotut.tex index dc1d568..2293564 100644 --- a/template_these_INSA_cotut.tex +++ b/template_these_INSA_cotut.tex @@ -1,6 +1,7 @@ \documentclass[a4paper,titlepage,12pt,french,twoside,openright]{report}
-\usepackage{graphicx}
+%\usepackage{graphicx}
+\usepackage[draft]{graphicx}
\usepackage{xcolor}
\usepackage[paper=a4paper,margin=2.5cm]{geometry}% http://ctan.org/pkg/geometry
\usepackage[pdftex,colorlinks=false]{hyperref}
@@ -79,6 +80,10 @@ pdfsubject={Th\`ese} %sous Acrobat. \input{remerciements}
\chapter*{Avertissement}
\input{avertissement}
+\chapter*{Notes}
+\input{notes}
+\chapter*{Notations}
+\input{notations}
\chapter{Introduction}
\section{Qu'est-ce que l'Intelligence Artificielle ?}
\label{sec:contexte-ckoi}
|