Private measurement of nonlinear correlations between data hosted across multiple parties
This work enables private exploratory data analysis and applications like feature screening and causal inference for entities handling sensitive distributed data, representing a novel but incremental advancement in multi-party privacy methods.
The paper tackles the problem of measuring nonlinear correlations between sensitive data hosted across multiple parties while preserving privacy, introducing a differentially private estimator for distance correlation with utility guarantees.
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.