User-Level Differential Privacy With Few Examples Per User
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka,, Chiyuan Zhang

TL;DR
This paper addresses user-level differential privacy in scenarios where each user has few examples, providing new algorithms that improve privacy-utility trade-offs and establish near-optimal bounds for various learning tasks.
Contribution
It introduces a generic transformation for approximate-DP and a new technique for pure-DP tailored to the example-scarce regime, improving bounds for multiple learning problems.
Findings
O(√m) savings in user count for approximate-DP algorithms
New bounds for private PAC learning and hypothesis selection
Near-optimal bounds for several user-level DP tasks
Abstract
Previous work on user-level differential privacy (DP) [Ghazi et al. NeurIPS 2021, Bun et al. STOC 2023] obtained generic algorithms that work for various learning tasks. However, their focus was on the example-rich regime, where the users have so many examples that each user could themselves solve the problem. In this work we consider the example-scarce regime, where each user has only a few examples, and obtain the following results: 1. For approximate-DP, we give a generic transformation of any item-level DP algorithm to a user-level DP algorithm. Roughly speaking, the latter gives a (multiplicative) savings of in terms of the number of users required for achieving the same utility, where is the number of examples per user. This algorithm, while recovering most known bounds for specific problems, also gives new bounds, e.g., for PAC learning.…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Stochastic Gradient Optimization Techniques
MethodsFocus
