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Differential Privacy (DP) is the de facto standard for reasoning about the privacy guarantees of a training algorithm.
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Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 3–18
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Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2018 · 2018
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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
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Auditing differentially private machine learning: How private is private sgd?
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Revisiting membership inference under realistic assumptions
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, and David Evans. 2020 · 2020
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Differentially private learning needs better features (or much more data)
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019 · 2019
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Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2019 · 2019
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Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan, and Shuang Song. 2019 · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Y Guan, Gregory Valiant, and James Zou. 2019 · 2019
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White-box vs black-box: Bayes optimal strategies for membership inference. In International Conference on Machine Learning . PMLR, 5558–5567
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. 2019 · 2019
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Demystifying membership inference attacks in machine learning as a service
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei. 2019 · 2019
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Machine unlearning: Linear filtration for logit-based classifiers
Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer. 2020 · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9304–9312
Aditya Golatkar, Alessandro Achille, and Stefano Soatto. 2020a
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Florian Tramèr and Dan Boneh. 2020 · 2020
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Membership Inference Attacks on Machine Learning: A Survey
Hongsheng Hu, Zoran Salcic, Gillian Dobbie, and Xuyun Zhang. 2021 · 2021
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Dataset inference: Ownership resolution in machine learning
Pratyush Maini, Mohammad Yaghini, and Nicolas Papernot. 2021 · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini. 2021 · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh. 2021 · 2021
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Unrolling sgd: Understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. 2021 · 2021
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Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms
Saeed Mahloujifar, Alexandre Sablayrolles, Graham Cormode, and Somesh Jha. 2022 · 2022
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