Work

Private independence testing across two parties

Year: 2022

Type: preprint

Abstract: We introduce $π$-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 Székely 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2207.03652

Authors: Vepakomma, Praneeth, Amiri, Mohammad Mohammadi, Canonne, Clément L., Raskar, Ramesh, Pentland, Alex

Institutions: Massachusetts Institute of Technology, The University of Sydney

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4285036337

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