학술논문
Inference Under Information Constraints III: Local Privacy Constraints
Document Type
Periodical
Author
Source
IEEE Journal on Selected Areas in Information Theory IEEE J. Sel. Areas Inf. Theory Selected Areas in Information Theory, IEEE Journal on. 2(1):253-267 Mar, 2021
Subject
Language
ISSN
2641-8770
Abstract
We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy of their data while enabling a central server to perform the tests. Under the notion of local differential privacy, we propose simple, sample-optimal, and communication-efficient protocols for these two questions in the noninteractive setting, where in addition users may or may not share a common random seed. In particular, we show that the availability of shared (public) randomness greatly reduces the sample complexity. Underlying our public-coin protocols are privacy-preserving mappings which, when applied to the samples, minimally contract the distance between their respective probability distributions.