Differentially Private Maximal Information Coefficients

Author name not available (Why is that?)

Publication date: 21 June 2022

Abstract: The Maximal Information Coefficient (MIC) is a powerful statistic to identify dependencies between variables. However, it may be applied to sensitive data, and publishing it could leak private information. As a solution, we present algorithms to approximate MIC in a way that provides differential privacy. We show that the natural application of the classic Laplace mechanism yields insufficient accuracy. We therefore introduce the MICr statistic, which is a new MIC approximation that is more compatible with differential privacy. We prove MICr is a consistent estimator for MIC, and we provide two differentially private versions of it. We perform experiments on a variety of real and synthetic datasets. The results show that the private MICr statistics significantly outperform direct application of the Laplace mechanism. Moreover, experiments on real-world datasets show accuracy that is usable when the sample size is at least moderately large.

Has companion code repository: https://github.com/jlazarsfeld/dp-mic

This page was built for publication: Differentially Private Maximal Information Coefficients

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6402785)