INSPECTRE: Privately Estimating the Unseen

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Jayadev Acharya
Gautam Kamath
https://orcid.org/0000-0003-0048-2559
Ziteng Sun
Huanyu Zhang

Abstract

We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to accuracy alpha, while maintaining epsilon-differential privacy of the sample. We prove almost-tight bounds on the sample size required for this problem for several functionals of interest, including support size, support coverage, and entropy. We show that the cost of privacy is negligible in a variety of settings, both theoretically and experimentally. Our methods are based on a sensitivity analysis of several state-of-the-art methods for estimating these properties with sublinear sample complexities

Article Details

How to Cite
Acharya, Jayadev, Gautam Kamath, Ziteng Sun, and Huanyu Zhang. 2020. “INSPECTRE: Privately Estimating the Unseen”. Journal of Privacy and Confidentiality 10 (2). https://doi.org/10.29012/jpc.724.
Section
TPDP 2018

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