The reusable holdout: Preserving validity in adaptive data analysis-Reference-Cited by-同舟云学术

The reusable holdout: Preserving validity in adaptive data analysis

Published:2015-08-07 Issue:6248 Volume:349 Page:636-638
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Dwork Cynthia¹,Feldman Vitaly²,Hardt Moritz³,Pitassi Toniann⁴,Reingold Omer⁵,Roth Aaron⁶

Affiliation:

1. Microsoft Research, Mountain View, CA 94043, USA.

2. IBM Almaden Research Center, San Jose, CA 95120, USA.

3. Google Research, Mountain View, CA 94043, USA.

4. Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada.

5. Samsung Research America, Mountain View, CA 94043, USA.

6. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

Testing hypotheses privately Large data sets offer a vast scope for testing already-formulated ideas and exploring new ones. Unfortunately, researchers who attempt to do both on the same data set run the risk of making false discoveries, even when testing and exploration are carried out on distinct subsets of data. Based on ideas drawn from differential privacy, Dwork et al. now provide a theoretical solution. Ideas are tested against aggregate information, whereas individual data set components remain confidential. Preserving that privacy also preserves statistical inference validity. Science , this issue p. 636

Funder

NSF

Alfred P. Sloan Foundation

Natural Sciences and Engineering Research Council of Canada

NSF CAREER

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference24 articles.

1. Controlling the false discovery rate – a practical and powerful approach to multiple testing;Benjamini Y.;J. R. Stat. Soc. B,1995

2. Why Most Published Research Findings Are False