A new class of private chi-square tests

Daniel Kifer; Ryan Rogers

A new class of private chi-square tests

Research output: Contribution to conference › Paper › peer-review

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 language	English (US)
State	Published - Jan 1 2017
Event	20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States Duration: Apr 20 2017 → Apr 22 2017

Conference

Conference	20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Country/Territory	United States
City	Fort Lauderdale
Period	4/20/17 → 4/22/17

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Statistics and Probability

Cite this

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title = "A new class of private chi-square tests",

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.",

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AB - 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.

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M3 - Paper

T2 - 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017

Y2 - 20 April 2017 through 22 April 2017

ER -

A new class of private chi-square tests

Abstract

Conference

All Science Journal Classification (ASJC) codes

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