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This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work.
The use of confidence or fiducial limits illustrated in the case of the binomial
C. J. Clopper and E. S. Pearson · 1934
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The MNIST database of handwritten digits
Y. LeCun, C. Cortez, and C. C. Burges · 1998
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Calibrating Noise to Sensitivity in Private Data Analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning Multiple Layers of Features from Tiny Images
A. Krizhevsky · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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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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Wide Residual Networks
S. Zagoruyko and N. Komodakis · 2016
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Learning with Privacy at Scale
Apple Differential Privacy Team · 2017
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Membership Inference Attacks against Machine Learning Models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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J. Dong, A. Roth, and W. J. Su · 2019
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TensorFlow Privacy
Google · 2019
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Evaluating differentially private machine learning in practice
B. Jayaraman and D. Evans · 2019
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White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, and H. Jégou · 2019
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Auditing Differentially Private Machine Learning: How Private is Private SGD?
M. Jagielski, J. Ullman, and A. Oprea · 2020
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fix prng key reuse in differential privacy example
M. Johnson · 2020
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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.
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
R. Chourasia, J. Ye, and R. Shokri · 2021
Cited alongside, same era.
Not All Noise is Accounted Equally: How Differentially Private Learning Benefits from Large Sampling Rates
F. Dörmann, O. Frisk, L. N. Andersen, and C. F. Pedersen · 2021
Cited alongside, same era.
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
M. Nasr, S. Songi, A. Thakurta, N. Papernot, and N. Carlini · 2021
Cited alongside, same era.
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Debugging Differential Privacy: A Case Study for Privacy Auditing
F. Tramer, A. Terzis, T. Steinke, S. Song, M. Jagielski, and N. Carlini · 2022
Later among the works it cites.
Enhanced Membership Inference Attacks against Machine Learning Models
J. Ye, A. Maddi, S. K. Murakonda, V. Bindschaedler, and R. Shokri · 2022
Later among the works it cites.
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
J. Ye and R. Shokri · 2022
Later among the works it cites.
Privacy Leakage at low sample size
T. Cebere · 2023
Later among the works it cites.
Group and Attack: Auditing Differential Privacy
J. Lokna, A. Paradis, D. I. Dimitrov, and M. Vechev · 2023
Later among the works it cites.
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
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N. Papernot, A. Thakurta, S. Song, S. Chien, and Ú. Erlingsson · 2021
Cited alongside, same era.
Differentially Private Learning Needs Better Features (or Much More Data)
F. Tramer and D. Boneh · 2021
Cited alongside, same era.
Opacus: User-friendly Differential Privacy Library in PyTorch
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, et al · 2021
Cited alongside, same era.
The 2020 census disclosure avoidance system topdown algorithm
J. M. Abowd, R. Ashmead, R. Cumings-Menon, S. Garfinkel, M. Heineck, C. Heiss, R. Johns, D. Kifer, P. Leclerc, A. Machanavajjhala, et al · 2022
Cited alongside, same era.
JAX-Privacy: Algorithms for Privacy-Preserving Machine Learning in JAX
B. Balle, L. Berrada, S. De, S. Ghalebikesabi, J. Hayes, A. Pappu, S. L. Smith, and R. Stanforth · 2022
Cited alongside, same era.
Membership Inference Attacks From First Principles
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer · 2022
Cited alongside, same era.
Unlocking High-Accuracy Differentially Private Image Classification through Scale
S. De, L. Berrada, J. Hayes, S. L. Smith, and B. Balle · 2022
Cited alongside, same era.
S. Maddock, A. Sablayrolles, and P. Stock · 2023
Later among the works it cites.
Tight Auditing of Differentially Private Machine Learning
M. Nasr, J. Hayes, T. Steinke, B. Balle, F. Tramèr, M. Jagielski, N. Carlini, and A. Terzis · 2023
Later among the works it cites.
Bayesian Estimation of Differential Privacy
S. Zanella-Béguelin, L. Wutschitz, S. Tople, A. Salem, V. Rühle, A. Paverd, M. Naseri, B. Köpf, and D. Jones · 2023
Later among the works it cites.
One-shot Empirical Privacy Estimation for Federated Learning
G. Andrew, P. Kairouz, S. Oh, A. Oprea, H. B. McMahan, and V. Suriyakumar · 2024
Closest in time.
It’s Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
M. S. M. S. Annamalai · 2024
Closest in time.
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
T. Cebere, A. Bellet, and N. Papernot · 2024
Closest in time.
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
S. Feng and F. Tramèr · 2024
Closest in time.
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
R. Liu, T. Wang, Y. Cao, and L. Xiong · 2024
Closest in time.
Privacy Auditing with One (1) Training Run
T. Steinke, M. Nasr, and M. Jagielski · 2024
Closest in time.
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
Y. Wen, L. Marchyok, S. Hong, J. Geiping, T. Goldstein, and N. Carlini · 2024
Closest in time.