Achieving GWAS with Homomorphic Encryption
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 proposed algorithm. Our solution mainly involves homomorphically encrypted matrix operations and suitable approximations that adapts the semi-parallel GWAS algorithm for HE. We leverage the complex space of the CKKS encryption scheme to increase the number of SNPs that can be packed within a ciphertext. We have also developed a cache module that manages ciphertexts, reducing the memory footprint. We have implemented our solution over two HE open source libraries, HEAAN and SEAL. Our best implementation took minutes for a dataset with samples, over covariates and SNPs. We demonstrate that it is possible to achieve GWAS with homomorphic encryption with suitable approximations.
Cite
@article{arxiv.1902.04303,
title = {Achieving GWAS with Homomorphic Encryption},
author = {Jun Jie Sim and Fook Mun Chan and Shibin Chen and Benjamin Hong Meng Tan and Khin Mi Mi Aung},
journal= {arXiv preprint arXiv:1902.04303},
year = {2019}
}