Private independence testing across two parties
Statistics Theory
2023-09-28 v2 Cryptography and Security
Machine Learning
Methodology
Statistics Theory
Abstract
We introduce -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.
Cite
@article{arxiv.2207.03652,
title = {Private independence testing across two parties},
author = {Praneeth Vepakomma and Mohammad Mohammadi Amiri and Clément L. Canonne and Ramesh Raskar and Alex Pentland},
journal= {arXiv preprint arXiv:2207.03652},
year = {2023}
}