Achieving GWAS with Homomorphic Encryption

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

Paper 2019/145 Original: eprint.iacr.org/2019/145 ↗

Achieving GWAS with Homomorphic Encryption

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.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Preprint.
History: 2019-08-01: last updated; 2019-02-14: published
Short URL: https://ia.cr/2019/145
License: https://creativecommons.org/licenses/by/4.0/

BibTeX

@misc{cryptoeprint:2019/145,
      author = {Jun Jie Sim and Fook Mun Chan and Shibin Chen and Benjamin Hong Meng Tan and Khin Mi Mi Aung},
      title = {Achieving GWAS with Homomorphic Encryption},
      howpublished = {Cryptology {ePrint} Archive, Paper 2019/145},
      year = {2019},
      url = {https://eprint.iacr.org/2019/145}
}