文章基本信息

标题：Differentially private false discovery rate control
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作者：Cynthia Dwork ; Weijie Su ; Li Zhang 等
期刊名称：Journal of Privacy and Confidentiality
出版年度：2021
卷号：11
期号：2
DOI：10.29012/jpc.755
语种：English
出版社：Carnegie Mellon University
摘要：Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedure for controlling the false discovery rate (FDR) in multiple hypothesis testing. Inspired by the Benjamini-Hochberg procedure (BHq), our approach is to first repeatedly add noise to the logarithms of the p-values to ensure differential privacy and to select an approximately smallest p-value serving as a promising candidate at each iteration; the selected p-values are further supplied to the BHq and our private procedure releases only the rejected ones. Moreover, we develop a new technique that is based on a backward submartingale for proving FDR control of a broad class of multiple testing procedures, including our private procedure, and both the BHq step- up and step-down procedures. As a novel aspect, the proof works for arbitrary dependence between the true null and false null test statistics, while FDR control is maintained up to a small multiplicative factor.
关键词：Differential privacy;Report Noisy Max;false discovery rate;Benjamini– Hochberg procedure;positive regression dependence on subset;submartingale