Paper 2019/145
Achieving GWAS with Homomorphic Encryption
Jun Jie Sim, Fook Mun Chan, Shibin Chen, 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.
Note: Added figures for Replicate and Duplicate.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint. MINOR revision.
- Keywords
- GWASHomomorphic EncryptionLogistic Regression
- Contact author(s)
- simjj @ i2r a-star edu sg
- History
- 2019-08-01: last of 2 revisions
- 2019-02-14: received
- See all versions
- Short URL
- https://ia.cr/2019/145
- License
-
CC BY
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}
}
