Private independence testing across two parties

Praneeth Vepakomma; Mohammad Mohammadi Amiri; Clément L. Canonne; Ramesh Raskar; Alex Pentland

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arXiv · 2207.03652

Private independence testing across two parties

Praneeth Vepakomma·Mohammad Mohammadi Amiri·Clément L. Canonne·Ramesh Raskar·Alex Pentland

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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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Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland. 2022-07-08. Private independence testing across two parties. https://arxiv.org/abs/2207.03652

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