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arXiv · 2101.07981

Inference under Information Constraints III: Local Privacy Constraints

Jayadev Acharya·Clément L. Canonne·Cody Freitag·Ziteng Sun·Himanshu Tyagi
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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.

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Jayadev Acharya, Clément L. Canonne, Cody Freitag, Ziteng Sun, Himanshu Tyagi. 2021-01-20. Inference under Information Constraints III: Local Privacy Constraints. https://arxiv.org/abs/2101.07981

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