Online Program Home
  My Program

Abstract Details

Activity Number: 205 - New Direction for Model Selection in Big Data
Type: Invited
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #322461
Title: Private False Discovery Rate Control
Author(s): Weijie Su* and Cynthia Dwork and Li Zhang
Companies: University of Pennsylvania and Harvard University and Google Inc.
Keywords: Differential privacy ; Benjamini-Hochberg procedure ; False discovery rate
Abstract:

We provide the first differentially private algorithms for controlling the false discovery rate (FDR) in multiple hypothesis testing. Our general approach is to adapt a well-known variant of the Benjamini-Hochberg procedure (BHq), making each step differentially private. This destroys the classical proof of FDR control. To prove FDR control of our method, we develop a new proof for the original (non-private) BHq procedure and its variants -- a proof requiring only the assumption that the true null test statistics are independent, allowing for arbitrary correlations between the true nulls and false nulls. This assumption is fairly weak compared to those previously shown in the vast literature on this topic, and explains in part the empirical robustness of BHq.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association