Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence TestingThanks: This work is part of the “Privacy Tools for Sharing Research Data” project based at Harvard, supported by NSF grant CNS-1237235 as well as a grant from the Sloan Foundation.
Marco Gaboardi Thanks: This work has been partially supported by the “PrivInfer - Programming Languages for Differential Privacy: Conditioning and Inference” EPSRC project EP/M022358/1 and by the University of Dundee, UK. Affiliation: University at Buffalo, SUNY Hyun woo Lim Affiliation: University of California, Los Angeles Ryan Rogers Affiliation: University of Pennsylvania Salil P. Vadhan Thanks: Also supported by a Simons Investigator grant. Work done in part while visiting the Department of Applied Mathematics and the Shing-Tung Yau Center at National Chiao-Tung University in Taiwan. Affiliation: Harvard University
Abstract
Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables.
原文 arXiv:1602.03090;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1602.03090v2