---
_id: '2011'
abstract:
- lang: eng
  text: The protection of privacy of individual-level information in genome-wide association
    study (GWAS) databases has been a major concern of researchers following the publication
    of “an attack” on GWAS data by Homer et al. (2008). Traditional statistical methods
    for confidentiality and privacy protection of statistical databases do not scale
    well to deal with GWAS data, 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 that 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, Uhler et al. (2013)
    proposed new methods to release aggregate GWAS data without compromising an individual’s
    privacy. We extend the methods developed in Uhler et al. (2013) for releasing
    differentially-private χ2χ2-statistics by allowing for arbitrary number of cases
    and controls, and for releasing differentially-private allelic test statistics.
    We also provide a new interpretation by assuming the controls’ data are known,
    which is a realistic assumption because some GWAS use publicly available data
    as controls. We assess the performance of the proposed methods through a risk-utility
    analysis on a real data set consisting of DNA samples collected by the Wellcome
    Trust Case Control Consortium and compare the methods with the differentially-private
    release mechanism proposed by Johnson and Shmatikov (2013).
acknowledgement: This research was partially supported by NSF Awards EMSW21-RTG and
  BCS-0941518 to the Department of Statistics at Carnegie Mellon University, and by
  NSF Grant BCS-0941553 to the Department of Statistics at Pennsylvania State University.
  This work was also supported in part by the National Center for Research Resources,
  Grant UL1 RR033184, and is now at the National Center for Advancing Translational
  Sciences, Grant UL1 TR000127 to Pennsylvania State University. The content is solely
  the responsibility of the authors and does not necessarily represent the official
  views of the NSF and NIH.
article_processing_charge: No
arxiv: 1
author:
- first_name: Fei
  full_name: Yu, Fei
  last_name: Yu
- first_name: Stephen
  full_name: Fienberg, Stephen
  last_name: Fienberg
- first_name: Alexandra
  full_name: Slaković, Alexandra
  last_name: Slaković
- first_name: Caroline
  full_name: Uhler, Caroline
  id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
  last_name: Uhler
  orcid: 0000-0002-7008-0216
citation:
  ama: Yu F, Fienberg S, Slaković A, Uhler C. Scalable privacy-preserving data sharing
    methodology for genome-wide association studies. <i>Journal of Biomedical Informatics</i>.
    2014;50:133-141. doi:<a href="https://doi.org/10.1016/j.jbi.2014.01.008">10.1016/j.jbi.2014.01.008</a>
  apa: Yu, F., Fienberg, S., Slaković, A., &#38; Uhler, C. (2014). Scalable privacy-preserving
    data sharing methodology for genome-wide association studies. <i>Journal of Biomedical
    Informatics</i>. Elsevier. <a href="https://doi.org/10.1016/j.jbi.2014.01.008">https://doi.org/10.1016/j.jbi.2014.01.008</a>
  chicago: Yu, Fei, Stephen Fienberg, Alexandra Slaković, and Caroline Uhler. “Scalable
    Privacy-Preserving Data Sharing Methodology for Genome-Wide Association Studies.”
    <i>Journal of Biomedical Informatics</i>. Elsevier, 2014. <a href="https://doi.org/10.1016/j.jbi.2014.01.008">https://doi.org/10.1016/j.jbi.2014.01.008</a>.
  ieee: F. Yu, S. Fienberg, A. Slaković, and C. Uhler, “Scalable privacy-preserving
    data sharing methodology for genome-wide association studies,” <i>Journal of Biomedical
    Informatics</i>, vol. 50. Elsevier, pp. 133–141, 2014.
  ista: Yu F, Fienberg S, Slaković A, Uhler C. 2014. Scalable privacy-preserving data
    sharing methodology for genome-wide association studies. Journal of Biomedical
    Informatics. 50, 133–141.
  mla: Yu, Fei, et al. “Scalable Privacy-Preserving Data Sharing Methodology for Genome-Wide
    Association Studies.” <i>Journal of Biomedical Informatics</i>, vol. 50, Elsevier,
    2014, pp. 133–41, doi:<a href="https://doi.org/10.1016/j.jbi.2014.01.008">10.1016/j.jbi.2014.01.008</a>.
  short: F. Yu, S. Fienberg, A. Slaković, C. Uhler, Journal of Biomedical Informatics
    50 (2014) 133–141.
date_created: 2018-12-11T11:55:12Z
date_published: 2014-08-01T00:00:00Z
date_updated: 2025-09-29T12:01:42Z
day: '01'
department:
- _id: CaUh
doi: 10.1016/j.jbi.2014.01.008
external_id:
  arxiv:
  - '1401.5193'
  isi:
  - '000340704200011'
intvolume: '        50'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://arxiv.org/abs/1401.5193
month: '08'
oa: 1
oa_version: Submitted Version
page: 133 - 141
publication: Journal of Biomedical Informatics
publication_status: published
publisher: Elsevier
publist_id: '5065'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable privacy-preserving data sharing methodology for genome-wide association
  studies
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 50
year: '2014'
...