A new class of private chi-square tests

Daniel Kifer, Ryan Rogers

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

In this paper, we develop new test statistics for private hypothesis testing. These statistics are designed specifically so that their asymptotic distributions, after accounting for noise added for privacy concerns, match the asymptotics of the classical (nonprivate) chi-square tests for testing if the multinomial data parameters lie in lower dimensional manifolds (examples include goodness of fit and independence testing). Empirically, these new test statistics outperform prior work, which focused on noisy versions of existing statistics.

Original languageEnglish (US)
StatePublished - Jan 1 2017
Event20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States
Duration: Apr 20 2017Apr 22 2017

Conference

Conference20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
CountryUnited States
CityFort Lauderdale
Period4/20/174/22/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Statistics and Probability

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    Kifer, D., & Rogers, R. (2017). A new class of private chi-square tests. Paper presented at 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, Fort Lauderdale, United States.