• Corpus ID: 232014629

Learning with User-Level Privacy

@inproceedings{Lvy2021LearningWU,
title={Learning with User-Level Privacy},
author={Daniel L{\'e}vy and Ziteng Sun and Kareem Amin and Satyen Kale and Alex Kulesza and Mehryar Mohri and Ananda Theertha Suresh},
booktitle={NeurIPS},
year={2021}
}
• Published in NeurIPS 23 February 2021
• Computer Science
We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samples, user-level DP protects a user's entire contribution ($m \ge 1$ samples), providing more stringent but more realistic protection against information leaks. We show that for high-dimensional mean estimation, empirical risk minimization with smooth losses, stochastic convex optimization, and learning hypothesis…
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References

SHOWING 1-10 OF 73 REFERENCES
Learning discrete distributions: user vs item-level privacy
• Computer Science
NeurIPS
• 2020
This work studies the fundamental problem of learning discrete distributions over $k$ symbols with user-level differential privacy and proposes a mechanism such that the number of users scales as $\tilde{\mathcal{O}}(k/(m\alpha^2) + k/\sqrt{m}\epsilon\alpha)$ and shows that it is nearly-optimal under certain regimes.
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
• Computer Science
The Annals of Statistics
• 2021
This paper investigates the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the classical low-dimensional and modern high-dimensional settings, and forms a general lower bound argument for minimax risks with differential privacy constraints.
User-Level Private Learning via Correlated Sampling
• Computer Science
ArXiv
• 2021
This work shows that, as long as each user receives sufficiently many samples, the authors can learn any privately learnable class via an (ε, δ)-DP algorithm using only O(log(1/δ)/ε) users, and shows a nearly-matching lower bound on the number of users required.
Smoothly Bounding User Contributions in Differential Privacy
• Computer Science
NeurIPS
• 2020
This work proposes a method which smoothly bounds user contributions by setting appropriate weights on data points and applies it to estimating the mean/quantiles, linear regression, and empirical risk minimization and shows that the algorithm provably outperforms the sample limiting algorithm.
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
• Computer Science
2014 IEEE 55th Annual Symposium on Foundations of Computer Science
• 2014
This work provides new algorithms and matching lower bounds for differentially private convex empirical risk minimization assuming only that each data point's contribution to the loss function is Lipschitz and that the domain of optimization is bounded.
Private Convex Empirical Risk Minimization and High-dimensional Regression
• Computer Science, Mathematics
COLT 2012
• 2012
This work significantly extends the analysis of the “objective perturbation” algorithm of Chaudhuri et al. (2011) for convex ERM problems, and gives the best known algorithms for differentially private linear regression.
Private Mean Estimation of Heavy-Tailed Distributions
• Mathematics, Computer Science
COLT
• 2020
Algorithms for the multivariate setting whose sample complexity is a factor of $O(d)$ larger than the univariate case are given, for which the sample simplicity is identical for all $k \geq 2$.
Private stochastic convex optimization: optimal rates in linear time
• Computer Science
STOC
• 2020
Two new techniques for deriving DP convex optimization algorithms both achieving the optimal bound on excess loss and using O(min{n, n 2/d}) gradient computations are described.
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
• Computer Science
ICML
• 2019
It is shown that in general there is a “sweet spot” that depends on measurable properties of the dataset, but that there is also a concrete cost to privacy that cannot be avoided simply by collecting more data.
Private Stochastic Convex Optimization with Optimal Rates
• Computer Science
NeurIPS
• 2019
The approach builds on existing differentially private algorithms and relies on the analysis of algorithmic stability to ensure generalization and implies that, contrary to intuition based on private ERM, private SCO has asymptotically the same rate of $1/\sqrt{n}$ as non-private SCO in the parameter regime most common in practice.