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--- a/biblio.bib
+++ b/biblio.bib
@@ -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},
generated by cgit v1.2.3 (git 2.25.1) at 2025-09-06 14:49:05 +0000