Privacy-Preserving Data Sharing for Genome-Wide Association Studies

The Journal of Privacy and Confidentiality

Stephen E. Fienberg¹  Aleksandra B. Slavkovic²  Caroline Uhler³ 

[1] Departmen t of Statistics, Machine Learning Department, Living analytics Research Center, Cylab, Carnegie Mellon University, Pittsburgh, PA;Department of Statistics, Department of Public Health Sciences, Penn State University, University Park, PA;InstituteofScienceandTechnologyAustria,AmCampus1,3400Klosterneuburg,Austria;

关键词: chi-squared statistics;    contingency tables;    differential privacy;    genome-wide association studies (GWAS);    logistic regression;    p-values;   

DOI  :  10.29012/jpc.v5i1.629

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown