Privacy preserving GWAS data sharing

Stephen E. Fienberg, Aleksandra Slavković, Carline Uhler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

Abstract

Traditional statistical methods for the confidentiality protection for statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases and external information on them. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach which provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information. Building on such notions, we propose new methods to release aggregate GWAS data without compromising an individual's privacy. We present methods for releasing differentially private minor allele frequencies, chisquare statistics and p-values. We compare these approaches on simulated data and on a GWAS study of canine hair length involving 685 dogs. We also propose a privacy-preserving method for finding genome-wide associations based on a differentially private approach to penalized logistic regression.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages628-635
Number of pages8
DOIs
StatePublished - 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 11 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/11/11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Fienberg, S. E., Slavković, A., & Uhler, C. (2011). Privacy preserving GWAS data sharing. In Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 (pp. 628-635). [6137439] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2011.140