Cryptology ePrint Archive: Report 2019/145

Achieving GWAS with Homomorphic Encryption

Jun Jie Sim and Fook Mun Chan and Shibin Chen and Benjamin Hong Meng Tan and Khin Mi Mi Aung

Abstract: One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as these genetic markers can be used to identify individuals uniquely. This problem is further exacerbated by a large number of SNPs needed, which produce reliable results at a higher risk of compromising the privacy of participants.

We describe a method using homomorphic encryption (HE) to perform GWAS in a secure and private setting. This work is based on a semi-parallel logistic regression algorithm proposed to accelerate GWAS computations. Our solution involves homomorphically encrypted matrices and suitable approximations that adapts the original algorithm to be HE-friendly. Our best implementation took $24.70$ minutes for a dataset with $245$ samples, $4$ covariates and $10643$ SNPs.

We demonstrate that it is possible to achieve GWAS with homomorphic encryption with suitable approximations.

Category / Keywords: applications / GWAS, Homomorphic Encryption, Logistic Regression

Date: received 12 Feb 2019, last revised 10 Mar 2019

Contact author: simjj at i2r a-star edu sg

Available format(s): PDF | BibTeX Citation

Note: Fixed wrong values in Depth of Homomorphic Operations

Version: 20190311:024147 (All versions of this report)

Short URL: ia.cr/2019/145


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