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Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight.
On stochastic limit and order relationships
H. B. Mann and A. Wald · 1943
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A useful convergence theorem for probability distributions
H. Scheffé · 1947
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Asymptotic theory of certain “goodness of fit” criteria based on stochastic processes
T. W. Anderson and D. A. Darling · 1952
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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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Concise formulas for the area and volume of a hyperspherical cap
S. Li · 2011
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2015
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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
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
B. Balle and Y.-X. Wang · 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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Property Testing for Differential Privacy
A. C. Gilbert and A. McMillan · 2018
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Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 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.
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases
X. Liu and S. Oh · 2019
Cited alongside, same era.
Secure single-server aggregation with (poly) logarithmic overhead
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova · 2020
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.
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
V. Feldman, A. McMillan, and K. Talwar · 2022
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Reconstructing training data from trained neural networks
N. Haim, G. Vardi, G. Yehudai, michal Irani, and O. Shamir · 2022
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A general framework for auditing differentially private machine learning
F. Lu, J. Munoz, M. Fuchs, T. LeBlond, E. V. Zaresky-Williams, E. Raff, F. Ferraro, and B. Testa · 2022
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Canife: Crafting canaries for empirical privacy measurement in federated learning
S. Maddock, A. Sablayrolles, and P. Stock · 2022
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Composition of differential privacy & privacy amplification by subsampling
T. Steinke · 2022
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S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečnỳ, S. Kumar, and H. B. McMahan · 2020
Cited alongside, same era.
Differentially private learning with adaptive clipping
G. Andrew, O. Thakkar, B. McMahan, and S. Ramaswamy · 2021
Cited alongside, same era.
Extracting training data from large language models
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel · 2021
Cited alongside, same era.
PPFL: privacy-preserving federated learning with trusted execution environments
F. Mo, H. Haddadi, K. Katevas, E. Marin, D. Perino, and N. Kourtellis · 2021
Cited alongside, same era.
Adversary instantiation: Lower bounds for differentially private machine learning
M. Nasr, S. Songi, A. Thakurta, N. Papemoti, and N. Carlin · 2021
Cited alongside, same era.
Reconstructing training data with informed adversaries
B. Balle, G. Cherubin, and J. Hayes · 2022
Cited alongside, same era.
Federated learning and privacy
K. Bonawitz, P. Kairouz, B. Mcmahan, and D. Ramage · 2022
Cited alongside, same era.
Multi-epoch matrix factorization mechanisms for private machine learning
C. A. Choquette-Choo, H. B. McMahan, K. Rush, and A. Thakurta · 2023
Closest in time.
Differential privacy in (a bit) more detail
D. Desfontaines · 2023
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Stronger privacy amplification by shuffling for Rényi and approximate differential privacy
V. Feldman, A. McMillan, and K. Talwar · 2023
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How to combine membership-inference attacks on multiple updated machine learning models
M. Jagielski, S. Wu, A. Oprea, J. Ullman, and R. Geambasu · 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
Closest in time.
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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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
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Unleashing the power of randomization in auditing differentially private ML
K. Pillutla, G. Andrew, P. Kairouz, H. B. McMahan, A. Oprea, and S. Oh · 2024
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Privacy auditing with one (1) training run
T. Steinke, M. Nasr, and M. Jagielski · 2024
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