---
_id: '2009'
abstract:
- lang: eng
  text: Traditional statistical methods for confidentiality protection of statistical
    databases do not scale well to deal with GWAS databases especially in terms of
    guarantees regarding protection from linkage to external information. 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, although
    the guarantees may come at a serious price in terms of data utility. 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, chi-square 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.
article_processing_charge: No
author:
- first_name: Caroline
  full_name: Uhler, Caroline
  id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
  last_name: Uhler
  orcid: 0000-0002-7008-0216
- first_name: Aleksandra
  full_name: Slavkovic, Aleksandra
  last_name: Slavkovic
- first_name: Stephen
  full_name: Fienberg, Stephen
  last_name: Fienberg
citation:
  ama: Uhler C, Slavkovic A, Fienberg S. Privacy-preserving data sharing for genome-wide
    association studies. <i>Journal of Privacy and Confidentiality </i>. 2013;5(1):137-166.
    doi:<a href="https://doi.org/10.29012/jpc.v5i1.629">10.29012/jpc.v5i1.629</a>
  apa: Uhler, C., Slavkovic, A., &#38; Fienberg, S. (2013). Privacy-preserving data
    sharing for genome-wide association studies. <i>Journal of Privacy and Confidentiality
    </i>. Carnegie Mellon University. <a href="https://doi.org/10.29012/jpc.v5i1.629">https://doi.org/10.29012/jpc.v5i1.629</a>
  chicago: Uhler, Caroline, Aleksandra Slavkovic, and Stephen Fienberg. “Privacy-Preserving
    Data Sharing for Genome-Wide Association Studies.” <i>Journal of Privacy and Confidentiality
    </i>. Carnegie Mellon University, 2013. <a href="https://doi.org/10.29012/jpc.v5i1.629">https://doi.org/10.29012/jpc.v5i1.629</a>.
  ieee: C. Uhler, A. Slavkovic, and S. Fienberg, “Privacy-preserving data sharing
    for genome-wide association studies,” <i>Journal of Privacy and Confidentiality
    </i>, vol. 5, no. 1. Carnegie Mellon University, pp. 137–166, 2013.
  ista: Uhler C, Slavkovic A, Fienberg S. 2013. Privacy-preserving data sharing for
    genome-wide association studies. Journal of Privacy and Confidentiality . 5(1),
    137–166.
  mla: Uhler, Caroline, et al. “Privacy-Preserving Data Sharing for Genome-Wide Association
    Studies.” <i>Journal of Privacy and Confidentiality </i>, vol. 5, no. 1, Carnegie
    Mellon University, 2013, pp. 137–66, doi:<a href="https://doi.org/10.29012/jpc.v5i1.629">10.29012/jpc.v5i1.629</a>.
  short: C. Uhler, A. Slavkovic, S. Fienberg, Journal of Privacy and Confidentiality  5
    (2013) 137–166.
date_created: 2018-12-11T11:55:11Z
date_published: 2013-08-01T00:00:00Z
date_updated: 2026-06-18T18:20:21Z
day: '01'
ddc:
- '000'
department:
- _id: CaUh
doi: 10.29012/jpc.v5i1.629
fulldoi: https://doi.org/10.29012/jpc.v5i1.629
intvolume: '         5'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://repository.cmu.edu/jpc/vol5/iss1/6
month: '08'
oa: 1
oa_version: Published Version
page: 137 - 166
publication: 'Journal of Privacy and Confidentiality '
publication_status: published
publisher: Carnegie Mellon University
publist_id: '5067'
quality_controlled: '1'
status: public
title: Privacy-preserving data sharing for genome-wide association studies
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 5
year: '2013'
...