Locally private hypothesis testing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

We initiate the study of differentially private hypothesis testing in the local-model, under both the standard (symmetric) randomized-response mechanism (Warner, 1965; Kasiviswanathan et al., 2008) and the newer (non-symmetric) mechanisms (Bassily & Smith, 2015; Bassily et al., 2017). First, we study the general framework of mapping each user's type into a signal and show that the problem of finding the maximum-likelihood distribution over the signals is feasible. Then we discuss the randomizedresponse mechanism and show that, in essence, it maps the null- and alternative-hypotheses onto new sets, an affine translation of the original sets. We then give sample complexity bounds for identity and independence testing under randomizedresponse. We then move to the newer nonsymmetric mechanisms and show that there too the problem of finding the maximum-likelihood distribution is feasible. Under the mechanism of Bassily et al (2017) we give identity and independence testers with better sample complexity than the testers in the symmetric case, and we also propose a χ2-based identity tester which we investigate empirically.

Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsAndreas Krause, Jennifer Dy
PublisherInternational Machine Learning Society (IMLS)
Pages7331-7340
Number of pages10
ISBN (Electronic)9781510867963
StatePublished - 2018
Externally publishedYes
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume10

Conference

Conference35th International Conference on Machine Learning, ICML 2018
Country/TerritorySweden
CityStockholm
Period10/07/1815/07/18

Bibliographical note

Publisher Copyright:
© 35th International Conference on Machine Learning, ICML 2018.All Rights Reserved.

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