Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge

BMC Medical Informatics and Decision Making

Research Article

Zhanglong Ji¹  Fei Yu² 

[1] Department of Computer Science and Engineering, University of California, San Diego, CA 92092, La Jolla, CA, USA;Department of Statistics, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, Pittsburgh, PA, USA;

关键词: χ;    Contingency table;    Differential privacy;    Genome-wide association study GWAS;    Data sharing;    Single-nucleotide polymorphism;   

DOI  :  10.1186/1472-6947-14-S1-S3

来源: Springer

【 摘 要 】

In response to the growing interest in genome-wide association study (GWAS) data privacy, the Integrating Data for Analysis, Anonymization and SHaring (iDASH) center organized the iDASH Healthcare Privacy Protection Challenge, with the aim of investigating the effectiveness of applying privacy-preserving methodologies to human genetic data. This paper is based on a submission to the iDASH Healthcare Privacy Protection Challenge. We apply privacy-preserving methods that are adapted from Uhler et al. 2013 and Yu et al. 2014 to the challenge's data and analyze the data utility after the data are perturbed by the privacy-preserving methods. Major contributions of this paper include new interpretation of the χ2 statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods.

【 授权许可】

CC BY   
© Yu and Ji; licensee BioMed Central Ltd. 2014