Private measurement of nonlinear correlations between data hosted across multiple parties

TL;DR

This paper presents the first differentially private estimator for nonlinear correlations, specifically distance correlation, applicable in multi-party settings, enabling privacy-preserving statistical analysis and inference.

Contribution

The paper introduces a novel differentially private estimator for nonlinear correlations in multi-party data, with utility guarantees and broad applications.

Findings

First private estimator for nonlinear correlations in multi-party data

Provides utility guarantees for the private estimator

Enables various private statistical tests and data analysis tasks

Abstract

We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provide utility guarantees of our private estimator. Ours is the first such private estimator of nonlinear correlations, to the best of our knowledge within a multi-party setup. The important measure of nonlinear correlation we consider is distance correlation. This work has direct applications to private feature screening, private independence testing, private k-sample tests, private multi-party causal inference and private data synthesis in addition to exploratory data analysis. Code access: A link to publicly access the code is provided in the supplementary file.