Cryptology ePrint Archive: Report 2019/223

Optimized Homomorphic Encryption Solution for Secure Genome-Wide Association Studies

Marcelo Blatt and Alexander Gusev and Yuriy Polyakov and Kurt Rohloff and Vinod Vaikuntanathan

Abstract: Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns.

We present a solution for secure GWAS using homomorphic encryption (HE) that keeps all individual data encrypted throughout the association study. Our solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: Secure Parallel Genome Wide Association Studies using HE''.

Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

Category / Keywords: implementation / implementation, public-key cryptography, RNS variant of approximate homomorphic encryption, genome-wide associate studies

Date: received 25 Feb 2019, last revised 1 Apr 2019

Contact author: ypolyakov at duality cloud

Available format(s): PDF | BibTeX Citation

Short URL: ia.cr/2019/223

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