path: root/ACSAC/paper.bib

@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} }