Fetching the paper…
Reading the bibliography…
How much does a machine learning algorithm leak about its training data, and why? Membership inference attacks are used as an auditing tool to quantify this leakage.
Evaluating differentially private machine learning in practice. In 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19) . 1895–1912
Bargav Jayaraman and David Evans. 2019 · 1912
Earlier work this paper cites.
Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 1897–1914
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. 2022 · 1914
Earlier work this paper cites.
Label-only membership inference attacks. In International Conference on Machine Learning . 1964–1974
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021 · 1974
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky. 1992 · 1992
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference . 265–284
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 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 · 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 · 2009
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) . Citeseer, 681–688
Max Welling and Yee W Teh. 2011 · 2011
Earlier work this paper cites.
Robust traceability from trace amounts. In Foundations of Computer Science (FOCS), 2015 IEEE 56th Annual Symposium on . 650–669
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan. 2015 · 2015
Earlier work this paper cites.
Membership privacy in MicroRNA-based studies. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . 319–330
Michael Backes, Pascal Berrang, Mathias Humbert, and Praveen Manoharan. 2016 · 2016
Earlier work this paper cites.
Exposed! a survey of attacks on private data
Cynthia Dwork, Adam Smith, Thomas Steinke, and Jonathan Ullman. 2017 · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models. In Security and Privacy (SP), 2017 IEEE Symposium on . 3–18
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Earlier work this paper cites.
Understanding membership inferences on well-generalized learning models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. 2018 · 2018
Earlier work this paper cites.
Membership Inference Attack against Differentially Private Deep Learning Model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganiere, Noman Mohammed, and Yang Wang. 2018 · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF) . 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19) . 267–284
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Cited alongside, same era.
Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan, and Shuang Song. 2019 · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE Symposium on Security and Privacy (SP) . 691–706
ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning
Sasi Kumar Murakonda and Reza Shokri. 2020 · 2020
Later among the works it cites.
On the privacy risks of algorithmic fairness. In 2021 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 292–303
Hongyan Chang and Reza Shokri. 2021 · 2021
Closest in time.
Membership leakage in label-only exposures. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . 880–895
Zheng Li and Yang Zhang. 2021 · 2021
Closest in time.
Antipodes of label differential privacy: Pate and alibi
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer. 2021 · 2021
Closest in time.
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models. In International Conference on Artificial Intelligence and Statistics . 2287–2295
Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
Cited alongside, same era.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. In IEEE Symposium on Security and Privacy (SP) . 1022–1036
M. Nasr, R. Shokri, and A. Houmansadr. 2019 · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference. In International Conference on Machine Learning . 5558–5567
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. 2019 · 2019
Cited alongside, same era.
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed Systems Security Symposium 2019 . Internet Society
Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes. 2019 · 2019
Cited alongside, same era.
Does learning require memorization? a short tale about a long tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing . 954–959
Vitaly Feldman. 2020 · 2020
Cited alongside, same era.
Differentially Private Learning Does Not Bound Membership Inference
Thomas Humphries, Matthew Rafuse, Lindsey Tulloch, Simon Oya, Ian Goldberg, Urs Hengartner, and Florian Kerschbaum. 2020 · 2020
Cited alongside, same era.
Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea. 2020 · 2020
Cited alongside, same era.
Stolen memories: Leveraging model memorization for calibrated white-box membership inference. In 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) . 1605–1622
Klas Leino and Matt Fredrikson. 2020 · 2020
Cited alongside, same era.
Closest in time.
Adversary instantiation: Lower bounds for differentially private machine learning. In 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 866–882
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlin. 2021 · 2021
Closest in time.
Systematic evaluation of privacy risks of machine learning models. In 30th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 21)
Liwei Song and Prateek Mittal. 2021 · 2021
Closest in time.
On memorization in probabilistic deep generative models
Gerrit van den Burg and Chris Williams. 2021 · 2021
Closest in time.
On the Importance of Difficulty Calibration in Membership Inference Attacks. In International Conference on Learning Representations
Lauren Watson, Chuan Guo, Graham Cormode, and Alexandre Sablayrolles. 2021 · 2021
Closest in time.
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, and Nicolas Papernot. 2022 · 2022
Closest in time.
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, and Nicholas Carlini. 2022 · 2022
Closest in time.
Debugging Differential Privacy: A Case Study for Privacy Auditing
Florian Tramer, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, and Nicholas Carlini. 2022 · 2022
Closest in time.