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A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training data.
Analyzing information leakage of updates to natural language models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt · 1912
Earlier work this paper cites.
Ix. on the problem of the most efficient tests of statistical hypotheses
Jerzy Neyman and Egon Sharpe Pearson · 1933
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig · 2008
Earlier work this paper cites.
Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig · 2008
Earlier work this paper cites.
Genomic privacy and limits of individual detection in a pool
Sriram Sankararaman, Guillaume Obozinski, Michael I Jordan, and Eran Halperin · 2009
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
Earlier work this paper cites.
Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Tfx: A tensorflow-based production-scale machine learning platform
Akshay Naresh Modi, Chiu Yuen Koo, Chuan Yu Foo, Clemens Mewald, Denis M. Baylor, Eric Breck, Heng-Tze Cheng, Jarek Wilkiewicz, Levent Koc, Lukasz Lew, Martin A. Zinkevich, Martin Wicke, Mustafa Ispir, Neoklis Polyzotis, Noah Fiedel, Salem Elie Haykal, Steven Whang, Sudip Roy, Sukriti Ramesh, Vihan Jain, Xin Zhang, and Zakaria Haque · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
The total variation distance between high-dimensional gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
Cited alongside, same era.
When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2020
Later among the works it cites.
Label-only membership inference attacks
Christopher A Choquette Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2020
Later among the works it cites.
Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Later among the works it cites.
Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson · 2020
Later among the works it cites.
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Machine learning with membership privacy using adversarial regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Cited alongside, same era.
Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Cited alongside, same era.
Eavesdrop the composition proportion of training labels in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 2019
Cited alongside, same era.
Zheng Li and Yang Zhang · 2020
Later among the works it cites.
Updates-leak: Data set inference and reconstruction attacks in online learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
Later among the works it cites.
Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2020
Later among the works it cites.
Analyzing information leakage of updates to natural language models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt · 2020
Later among the works it cites.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2021
Later among the works it cites.
Revisiting membership inference under realistic assumptions
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, and David Evans · 2021
Later among the works it cites.
Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini · 2021
Later among the works it cites.
Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
Later among the works it cites.