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Title: Differentially private false discovery rate control
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.  more » « less
Award ID(s):
1763665
NSF-PAR ID:
10310347
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Journal of Privacy and Confidentiality
Volume:
11
Issue:
2
ISSN:
2575-8527
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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