Disparate Vulnerability to Membership Inference Attacks

[1] Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. Differential privacy has disparate impact on model accuracy. In Annual Conference on Neural Information Processing Systems, NeurIPS, 2019. Search in Google Scholar

[2] Solon Barocas and Andrew D Selbst. Big data’s disparate impact. Calif. L. Rev., 2016.10.2139/ssrn.2477899 Search in Google Scholar

[3] Arindrajit Basu, Elonnai Hickok, and Aditya Singh Chawala. The Localisation Gambit: Unpacking Policy Measures for Sovereign Control of Data in India. Centre for Internet and Society, India, 2019. Search in Google Scholar

[4] Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, 2018.10.1177/0049124118782533 Search in Google Scholar

[5] Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report MSR-TR-2020-32, Microsoft, May 2020. URL https://www.microsoft.com/en-us/research/publication/fairlearn-atoolkit-for-assessing-and-improving-fairness-in-ai/. Search in Google Scholar

[6] Hongyan Chang and Reza Shokri. On the privacy risks of algorithmic fairness. IEEE European Symposium on Security and Privacy, EuroS&P, 2021.10.1109/EuroSP51992.2021.00028 Search in Google Scholar

[7] Konstantinos Chatzikokolakis, Giovanni Cherubin, Catuscia Palamidessi, and Carmela Troncoso. The Bayes security measure. arXiv preprint arXiv:2011.03396, 2020. Search in Google Scholar

[8] Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate. Differentially private empirical risk minimization. J. Mach. Learn. Res., 2011. Search in Google Scholar

[9] Giovanni Cherubin, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. F-BLEAU: Fast black-box leakage estimation. In IEEE Symposium on Security and Privacy, S&P, 2019.10.1109/SP.2019.00073 Search in Google Scholar

[10] Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 2017.10.1089/big.2016.004728632438 Search in Google Scholar

[11] Alexandra Chouldechova and Aaron Roth. The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810, 2018. Search in Google Scholar

[12] Luc Devroye, László Györfi, and Gábor Lugosi. A probabilistic theory of pattern recognition, volume 31. Springer Science & Business Media, 2013. Search in Google Scholar

[13] Cynthia Dwork. Differential Privacy. Springer US, 2011.10.4016/26354.01 Search in Google Scholar

[14] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Rein-gold, and Richard S. Zemel. Fairness through awareness. In Innovations in Theoretical Computer Science, 2012.10.1145/2090236.2090255 Search in Google Scholar

[15] Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. Privacy for all: Ensuring fair and equitable privacy protections. In Conference on Fairness, Accountability and Transparency, FAT, 2018. Search in Google Scholar

[16] Farhad Farokhi and Mohamed Ali Kaafar. Modelling and quantifying membership information leakage in machine learning. arXiv preprint arXiv:2001.10648, 2020. Search in Google Scholar

[17] Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236, 2016. Search in Google Scholar

[18] Graham Greenleaf and Bertil Cottier. 2020 ends a decade of 62 new data privacy laws. Privacy Laws & Business International Report, 2020. Search in Google Scholar

[19] Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. In NIPS, 2016. Search in Google Scholar

[20] Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher. Diffprivlib: The IBM differential privacy library. arXiv preprint arXiv:1907.02444, 2019. Search in Google Scholar

[21] Thomas Humphries, Matthew Rafuse, Lindsey Tulloch, Simon Oya, Ian Goldberg, Urs Hengartner, and Florian Kerschbaum. Differentially private learning does not bound membership inference. arXiv preprint arXiv:2010.12112, 2020. Search in Google Scholar

[22] Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu. Revisiting membership inference under realistic assumptions. Proceedings on Privacy Enhancing Technologies, 2021.10.2478/popets-2021-0031 Search in Google Scholar

[23] Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, and Linda Sellie. On the learnability of discrete distributions. In ACM Symposium on Theory of Computing, 1994.10.1145/195058.195155 Search in Google Scholar

[24] Amir E Khandani, Adlar J Kim, and Andrew W Lo. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 2010.10.2139/ssrn.1568864 Search in Google Scholar

[25] Ron Kohavi. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In International Conference on Knowledge Discovery and Data Mining, KDD, 1996. Search in Google Scholar

[26] Klas Leino and Matt Fredrikson. Stolen memories: Leveraging model memorization for calibrated white-box membership inference. In Srdjan Capkun and Franziska Roesner, editors, USENIX Security Symposium, 2020. Search in Google Scholar

[27] Jiacheng Li, Ninghui Li, and Bruno Ribeiro. Membership inference attacks and defenses in classification models. In CODASPY, 2021. Search in Google Scholar

[28] Zachary C. Lipton, Julian McAuley, and Alexandra Chouldechova. Does mitigating ML’s impact disparity require treatment disparity? In Annual Conference on Neural Information Processing Systems,NeurIPS, 2018. Search in Google Scholar

[29] Yunhui Long, Lei Wang, Diyue Bu, Vincent Bindschaedler, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. A pragmatic approach to membership inferences on machine learning models. In IEEE European Symposium on Security and Privacy, EuroS&P, 2020.10.1109/EuroSP48549.2020.00040 Search in Google Scholar

[30] Kristian Lum and William Isaac. To predict and serve? Significance, 2016. Search in Google Scholar

[31] Preetum Nakkiran and Yamini Bansal. Distributional generalization: A new kind of generalization. arXiv preprint arXiv:2009.08092, 2020. Search in Google Scholar

[32] Milad Nasr, Reza Shokri, and Amir Houmansadr. Comprehensive privacy analysis of deep learning: Stand-alone and federated learning under passive and active white-box inference attacks. In IEEE Symposium on Security and Privacy, S&P, 2018. Search in Google Scholar

[33] Ziad Obermeyer and Ezekiel J Emanuel. Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 2016.10.1056/NEJMp1606181507053227682033 Search in Google Scholar

[34] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011. Search in Google Scholar

[35] David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau. Fair decision making using privacy-protected data. In Conference on Fairness, Accountability, and Transparency, FAT*, 2020.10.1145/3351095.3372872 Search in Google Scholar

[36] Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. White-box vs black-box: Bayes optimal strategies for membership inference. In International Conference on Machine Learning, ICML, 2019. Search in Google Scholar

[37] Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. ML-leaks: Model and data independent membership inference attacks and defenses on machine learning models. In 26th Annual Network and Distributed System Security Symposium, NDSS, 2019.10.14722/ndss.2019.23119 Search in Google Scholar

[38] Howard J Seltman. Experimental design and analysis. 2012. Search in Google Scholar

[39] Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In IEEE Symposium on Security and Privacy, S&P, 2017.10.1109/SP.2017.41 Search in Google Scholar

[40] Reza Shokri, Martin Strobel, and Yair Zick. On the privacy risks of model explanations. arXiv preprint arXiv:1907.00164, 2019. Search in Google Scholar

[41] Liwei Song and Prateek Mittal. Systematic evaluation of privacy risks of machine learning models. In USENIX Security Symposium, 2021. Search in Google Scholar

[42] Michael Veale, Reuben Binns, and Lilian Edwards. Algorithms that remember: model inversion attacks and data protection law. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2018. Search in Google Scholar

[43] Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. Privacy risk in machine learning: Analyzing the connection to overfitting. In IEEE Computer Security Foundations Symposium, CSF, 2018.10.1109/CSF.2018.00027 Search in Google Scholar

[44] Han Zhao and Geoffrey J. Gordon. Inherent tradeoffs in learning fair representations. In Annual Conference on Neural Information Processing Systems, NeurIPS, 2019. Search in Google Scholar

• Understanding Privacy-Related Advice on Stack Overflow

• Revisiting Identification Issues in GDPR ‘Right Of Access’ Policies: A Technical and Longitudinal Analysis

• Employees’ privacy perceptions: exploring the dimensionality and antecedents of personal data sensitivity and willingness to disclose

• Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases

• Analyzing the Feasibility and Generalizability of Fingerprinting Internet of Things Devices

• CoverDrop: Blowing the Whistle Through A News App

• Building a Privacy-Preserving Smart Camera System

• FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

• Are iPhones Really Better for Privacy? A Comparative Study of iOS and Android Apps

• How to prove any NP statement jointly? Efficient Distributed-prover Zero-Knowledge Protocols

• Editors’ Introduction

• PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics

• Who Knows I Like Jelly Beans? An Investigation Into Search Privacy

• SoK: Plausibly Deniable Storage

• d3p - A Python Package for Differentially-Private Probabilistic Programming

• Updatable Private Set Intersection

• Knowledge Cross-Distillation for Membership Privacy

• RegulaTor: A Straightforward Website Fingerprinting Defense

• Privacy-Preserving Positioning in Wi-Fi Fine Timing Measurement

• Efficient Set Membership Proofs using MPC-in-the-Head

• Checking Websites’ GDPR Consent Compliance for Marketing Emails

• Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

• Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

• User-Level Label Leakage from Gradients in Federated Learning

• Privacy-preserving training of tree ensembles over continuous data

• Differentially Private Simple Linear Regression

• Increasing Adoption of Tor Browser Using Informational and Planning Nudges