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Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of analgorithm, while empirical auditing establishes a practical lower bound.
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I. Mironov · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Divergence measures estimation and its asymptotic normality theory using wavelets empirical processes i
A. D. Ba, G. S. Lo, and D. Ba · 2018
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
B. Balle, G. Barthe, and M. Gaboardi · 2018
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Detecting violations of differential privacy
Z. Ding, Y. Wang, G. Wang, D. Zhang, and D. Kifer · 2018
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Privacy-preserving prediction
C. Dwork and V. Feldman · 2018
Antipodes of label differential privacy: Pate and alibi
M. Malek Esmaeili, I. Mironov, K. Prasad, I. Shilov, and F. Tramer · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
M. Nasr, S. Songi, A. Thakurta, N. Papernot, and N. Carlin · 2021
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Tempered sigmoid activations for deep learning with differential privacy
N. Papernot, A. Thakurta, S. Song, S. Chien, and Ú. Erlingsson · 2021
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Multi-epoch matrix factorization mechanisms for private machine learning
C. A. Choquette-Choo, H. B. McMahan, K. Rush, and A. Thakurta · 2022
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Unlocking high-accuracy differentially private image classification through scale
S. De, L. Berrada, J. Hayes, S. L. Smith, and B. Balle · 2022
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Scalable private learning with pate
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and U. Erlingsson · 2018
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Roberta: A robustly optimized bert pretraining approach
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov · 2019
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Hypothesis testing interpretations and renyi differential privacy
B. Balle, G. Barthe, M. Gaboardi, J. Hsu, and T. Sato · 2020
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Capc learning: Confidential and private collaborative learning
C. A. Choquette-Choo, N. Dullerud, A. Dziedzic, Y. Zhang, S. Jha, N. Papernot, and X. Wang · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Witches’ brew: Industrial scale data poisoning via gradient matching
J. Geiping, L. H. Fowl, W. R. Huang, W. Czaja, G. Taylor, M. Moeller, and T. Goldstein · 2020
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Improved differential privacy for sgd via optimal private linear operators on adaptive streams
S. Denisov, H. B. McMahan, J. Rush, A. Smith, and A. Guha Thakurta · 2022
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A general framework for auditing differentially private machine learning
F. Lu, J. Munoz, M. Fuchs, T. LeBlond, E. Zaresky-Williams, E. Raff, F. Ferraro, and B. Testa · 2022
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Truth serum: Poisoning machine learning models to reveal their secrets
F. Tramèr, R. Shokri, A. San Joaquin, H. Le, M. Jagielski, S. Hong, and N. Carlini · 2022
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Debugging differential privacy: A case study for privacy auditing
F. Tramer, A. Terzis, T. Steinke, S. Song, M. Jagielski, and N. Carlini · 2022
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In differential privacy, there is truth: on vote-histogram leakage in ensemble private learning
J. Wang, R. Schuster, I. Shumailov, D. Lie, and N. Papernot · 2022
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One-shot empirical privacy estimation for federated learning
G. Andrew, P. Kairouz, S. Oh, A. Oprea, H. B. McMahan, and V. Suriyakumar · 2023
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Flocks of stochastic parrots: Differentially private prompt learning for large language models
H. Duan, A. Dziedzic, N. Papernot, and F. Boenisch · 2023
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Students parrot their teachers: Membership inference on model distillation
M. Jagielski, M. Nasr, C. Choquette-Choo, K. Lee, and N. Carlini · 2023
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A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al · 2023
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R \ \backslash ’enyitester: A variational approach to testing differential privacy
W. Kong, A. M. Medina, and M. Ribero · 2023
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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
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Privacy auditing with one (1) training run
T. Steinke, M. Nasr, and M. Jagielski · 2023
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Federated learning of gboard language models with differential privacy
Z. Xu, Y. Zhang, G. Andrew, C. A. Choquette-Choo, P. Kairouz, H. B. McMahan, J. Rosenstock, and Y. Zhang · 2023
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