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New methods designed to preserve data privacy require careful scrutiny.
“Membership inference attacks from first principles”
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis and Florian Tramer · 1914
Earlier work this paper cites.
“Deep learning with differential privacy”
Martin Abadi, Andy Chu, Ian Goodfellow, H McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
Earlier work this paper cites.
“Membership inference attacks against machine learning models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz and Michael Backes · 2018
Earlier work this paper cites.
“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
Earlier work this paper cites.
“White-box vs black-box: Bayes optimal strategies for membership inference”
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier and Herv“’e J“’egou · 2019
Cited alongside, same era.
“Auditing differentially private machine learning: How private is private sgd?”
Matthew Jagielski, Jonathan Ullman and Alina Oprea · 2020
Cited alongside, same era.
“Differentially private learning needs better features (or much more data)”
Florian Tramer and Dan Boneh · 2020
Cited alongside, same era.
“Adversary instantiation: Lower bounds for differentially private machine learning”
Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papernot and Nicholas Carlin · 2021
Cited alongside, same era.
“Systematic evaluation of privacy risks of machine learning models”
Liwei Song and Prateek Mittal · 2021
Later among the works it cites.
“Dataset Condensation with Distribution Matching”
Bo Zhao and Hakan Bilen · 2021
Later among the works it cites.
“Unlocking high-accuracy differentially private image classification through scale”
Soham De, Leonard Berrada, Jamie Hayes, Samuel Smith and Borja Balle · 2022
Closest in time.
“Privacy for Free: How does Dataset Condensation Help Privacy?”
Tian Dong, Bo Zhao and Lingjuan Lyu · 2022
Closest in time.
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