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

Abstract

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.

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS

“…SNPs) before computation (Wang et al, 2014) or to the research outcomes (e.g. P-value or test statistics) obtained after computation (Yu and Ji, 2014). To compare with the methods of applying DP before computation (DPBC) and DP after computation (DPAC), we select a KD dataset with 30 records and 744 SNPs.…”

“…Based on the idea of DPAC in (Yu and Ji, 2014), we also derived the corresponding DP algorithm for exact logistic regression in Supplementary Section S9. The results in terms of recall and precision in DPAC are better than those of DPBC in Table 6.…”

“…A number of technical solutions (Ayday et al, 2013;Bos et al, 2014;Cheon et al, 2015;Jiang et al, 2014;Kamm et al, 2013;Lauter et al, 2014;Naveed et al, 2014;Wang et al, 2014;Xie et al, 2014;Yu and Ji, 2014) have been proposed to protect genome privacy in data analysis. Existing studies can be categorized into two groups: (i) protecting the computation process (Cheon et al, 2015;Humbert et al, 2013;Lauter et al, 2014) in genome data analysis, and (ii) protecting the genome data before computation (Wang et al, 2014;Zhao et al, 2015) or research outcomes after computation (Yu and Ji, 2014).…”

“…Existing studies can be categorized into two groups: (i) protecting the computation process (Cheon et al, 2015;Humbert et al, 2013;Lauter et al, 2014) in genome data analysis, and (ii) protecting the genome data before computation (Wang et al, 2014;Zhao et al, 2015) or research outcomes after computation (Yu and Ji, 2014). In this work, we focus on the protection of the computation process of rare variants analysis in GWAS.…”

Secure Testing for Genetic Diseases on Encrypted Genomes with Homomorphic Encryption Scheme

“…There are a number of technical solutions that have been proposed to protect genome privacy, and existing studies can be categorized into two groups [4]: (i) protecting the 2 Security and Communication Networks computation process in genome data analysis [5][6][7] and (ii) protecting the genome data before computation [8,9] or research outcomes after computation [10].…”