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Inference Under Information Constraints III: Local Privacy Constraints | IEEE Journals & Magazine | IEEE Xplore

Inference Under Information Constraints III: Local Privacy Constraints


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 t...Show More

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.
Published in: IEEE Journal on Selected Areas in Information Theory ( Volume: 2, Issue: 1, March 2021)
Page(s): 253 - 267
Date of Publication: 22 January 2021
Electronic ISSN: 2641-8770

Funding Agency:


References

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