Private independence testing across two parties

• URL: http://arxiv.org/abs/2207.03652v2
• Date: Tue, 26 Sep 2023 23:46:02 GMT
• Title: Private independence testing across two parties
• Authors: Praneeth Vepakomma, Mohammad Mohammadi Amiri, Cl\'ement L. Canonne, Ramesh Raskar, Alex Pentland
• Abstract summary: $pi$-test is a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties. We establish both additive and multiplicative error bounds on the utility of our differentially private test.
• Score: 21.236868468146348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce $\pi$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties. Our algorithm relies on privately estimating the distance correlation between datasets, a quantitative measure of independence introduced in Sz\'ekely et al. [2007]. We establish both additive and multiplicative error bounds on the utility of our differentially private test, which we believe will find applications in a variety of distributed hypothesis testing settings involving sensitive data.

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