2021/10/18 by Praneeth Vepakomma, Vepakomma, Praneeth, Subha Nawer Pushpita +3
Computer Science · Mathematics · #Computation (stat.CO) #Cryptography and Data Security #Cryptography and Security (cs.CR) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Random Matrices and Applications #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2110.09670
openalex publication_date 2021/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.