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

被引：0

作者：

Gaboardi, Marco ^{[1
]}

Lim, Hyun Woo ^{[2
]}

Rogers, Ryan ^{[3
]}

Vadhan, Salil P. ^{[4
]}

机构：

[1] SUNY Buffalo, Buffalo, NY 14201 USA

[2] Univ Calif Los Angeles, Los Angeles, CA USA

[3] Univ Penn, Philadelphia, PA 19104 USA

[4] Harvard Univ, Cambridge, MA 02138 USA

来源：

INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 48 | 2016年 / 48卷

基金：

英国工程与自然科学研究理事会; 美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：10

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