--- 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@ ...