Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

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Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

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Title: Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
Author: Gaboardi, Marco; Lim, Hyun-Woo; Rogers, Ryan M.; Vadhan, Salil P.

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Citation: Gaboardi, Marco, Hyun-Woo Lim, Ryan M. Rogers, and Salil P. Vadhan. 2016. "Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing." In ICML'16 Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, June 19-24, 2016, Volume 48: 2111-2120.
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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.
Other Sources: https://arxiv.org/abs/1602.03090
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34614371
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