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Algorithms such as Differentially Private SGD enable training machine learning models with formal privacy guarantees.
Ú. Erlingsson, I. Mironov, A. Raghunathan, and S. Song · 1908
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On the intrinsic privacy of stochastic gradient descent
S. L. Hyland and S. Tople · 1912
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One-sided confidence intervals in discrete distributions
T. Tony Cai · 2004
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Investigating membership inference attacks under data dependencies
T. Humphries, S. Oya, L. Tulloch, M. Rafuse, I. Goldberg, U. Hengartner, and F. Kerschbaum · 2010
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No free lunch in data privacy
D. Kifer and A. Machanavajjhala · 2011
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Differential privacy for functions and functional data
R. Hall, A. Rinaldo, and L. A. Wasserman · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts · 2013
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Detecting violations of differential privacy
Z. Ding, Y. Wang, G. Wang, D. Zhang, and D. Kifer · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
B. Jayaraman and D. Evans · 2019
Is private learning possible with instance encoding?
N. Carlini, S. Deng, S. Garg, S. Jha, S. Mahloujifar, M. Mahmoody, S. Song, A. Thakurta, and F. Tramèr · 2021
Later among the works it cites.
Differential privacy dynamics of Langevin diffusion and noisy gradient descent
R. Chourasia, J. Ye, and R. Shokri · 2021
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Numerical composition of differential privacy
S. Gopi, Y. T. Lee, and L. Wutschitz · 2021
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A robustly optimized BERT pre-training approach with post-training
Z. Liu, W. Lin, Y. Shi, and J. Zhao · 2021
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Antipodes of label differential privacy: PATE and ALIBI
M. Malek, I. Mironov, K. Prasad, I. Shilov, and F. Tramèr · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
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Cited alongside, same era.
Auditing data provenance in text-generation models
C. Song and V. Shmatikov · 2019
Cited alongside, same era.
Auditing differentially private machine learning: How private is private SGD?
M. Jagielski, J. Ullman, and A. Oprea · 2020
Cited alongside, same era.
Analyzing information leakage of updates to natural language models
S. Zanella-Béguelin, L. Wutschitz, S. Tople, V. Rühle, A. Paverd, O. Ohrimenko, B. Köpf, and M. Brockschmidt · 2020
Cited alongside, same era.
DP-Sniper: Black-box discovery of differential privacy violations using classifiers
B. Bichsel, S. Steffen, I. Bogunovic, and M. Vechev · 2021
Cited alongside, same era.
M. Nasr, S. Songi, A. Thakurta, N. Papemoti, and N. Carlini · 2021
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Enhanced membership inference attacks against machine learning models
J. Ye, A. Maddi, S. K. Murakonda, and R. Shokri · 2021
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Membership inference attacks from first principles
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer · 2022
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A list of real-world uses of differential privacy
D. Desfontaines · 2022
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Disparate vulnerability to membership inference attacks
M. Yaghini, B. Kulynych, G. Cherubin, M. Veale, and C. Troncoso · 2022
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