---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Caroline
      foaf_name: Uhler, Caroline
      foaf_surname: Uhler
      foaf_workInfoHomepage: http://www.librecat.org/personId=49ADD78E-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-7008-0216
  - foaf_Person:
      foaf_givenName: Aleksandra
      foaf_name: Slavkovic, Aleksandra
      foaf_surname: Slavkovic
  - foaf_Person:
      foaf_givenName: Stephen
      foaf_name: Fienberg, Stephen
      foaf_surname: Fienberg
  bibo_doi: 10.29012/jpc.v5i1.629
  bibo_issue: '1'
  bibo_volume: 5
  dct_date: 2013^xs_gYear
  dct_language: eng
  dct_publisher: Carnegie Mellon University@
  dct_title: Privacy-preserving data sharing for genome-wide association studies@
...