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We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints.
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Advances and open problems in federated learning
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
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C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
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The algorithmic foundations of differential privacy
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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On lower complexity bounds for large-scale smooth convex optimization
Private stochastic convex optimization with optimal rates
R. Bassily, V. Feldman, K. Talwar, and A. G. Thakurta · 2019
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Average-case averages: Private algorithms for smooth sensitivity and mean estimation
M. Bun and T. Steinke · 2019
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The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T. T. Cai, Y. Wang, and L. Zhang · 2019
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Stochastic model-based minimization of weakly convex functions
D. Davis and D. Drusvyatskiy · 2019
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Enabling developers and organizations to use differential privacy, 2019
Google · 2019
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A short note on concentration inequalities for random vectors with subgaussian norm
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C. Guzmán and A. Nemirovski · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
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M. Braverman, A. Garg, T. Ma, H. L. Nguyen, and D. P. Woodruff · 2016
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Katyusha: the first direct acceleration of stochastic gradient methods
Z. Allen-Zhu · 2017
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Learning with privacy at scale, 2017
Apple Privacy Team · 2017
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Collecting telemetry data privately
B. Ding, J. Kulkarni, and S. Yekhanin · 2017
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C. Jin, P. Netrapalli, R. Ge, S. M. Kakade, and M. I. Jordan · 2019
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Privately learning high-dimensional distributions
G. Kamath, J. Li, V. Singhal, and J. Ullman · 2019
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Private selection from private candidates
J. Liu and K. Talwar · 2019
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High Dimensional Probability: An Introduction with Applications in Data Science
R. Vershynin · 2019
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint
M. J. Wainwright · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi · 2019
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Stability of stochastic gradient descent on nonsmooth convex losses
R. Bassily, V. Feldman, C. Guzmán, and K. Talwar · 2020
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Smoothly bounding user contributions in differential privacy
A. Epasto, M. Mahdian, J. Mao, V. Mirrokni, and L. Ren · 2020
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Private stochastic convex optimization: optimal rates in linear time
V. Feldman, T. Koren, and K. Talwar · 2020
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Private mean estimation of heavy-tailed distributions
G. Kamath, V. Singhal, and J. Ullman · 2020
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Estimate sequences for stochastic composite optimization: Variance reduction, acceleration, and robustness to noise
A. Kulunchakov and J. Mairal · 2020
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Learning discrete distributions: user vs item-level privacy
Y. Liu, A. Theertha Suresh, F. Yu, S. Kumar, and M. Riley · 2020
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Differentially private SQL with bounded user contribution
R. J. Wilson, C. Y. Zhang, W. Lam, D. Desfontaines, D. Simmons-Marengo, and B. Gipson · 2020
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Minibatch vs local SGD for heterogeneous distributed learning
B. Woodworth, K. K. Patel, and N. Srebro · 2020
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Differentially private Assouad, Fano, and Le Cam
J. Acharya, Z. Sun, and H. Zhang · 2021
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User-level private learning via correlated sampling
B. Ghazi, R. Kumar, and P. Manurangsi · 2021
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