blob: 8b1c7130253654ba903030b4cedc7db0e4c59de0 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
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.}
|