Inference Under Information Constraints III: Local Privacy Constraints

By Jayadev Acharya, Clément Louis Canonne, Cody R. Freitag, Ziteng Sun, Himanshu Tyagi. 2021

40 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Unified lower bounds for interactive high-dimensional estimation under information constraints (2020, background): “In particular, for ̺ ∈ (0,1] (high-privacy regime) we retrieve the lower bound established in [2], which matches the upper bound from [1].”