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Title:
Achieving GWAS with Homomorphic Encryption
Authors: Jun Jie Sim, Fook Mun Chan, Shibin Chen, Benjamin Hong Meng Tan, 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.
ePrint: https://eprint.iacr.org/2019/145
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