Paper 2019/152

Privacy-preserving Approximate GWAS computation based on Homomorphic Encryption

Duhyeong Kim, Yongha Son, Dongwoo Kim, Andrey Kim, Seungwan Hong, and Jung Hee Cheon

Abstract

One of three tasks in a secure genome analysis competition called IDASH 2018 was to develop a solution for privacy-preserving GWAS computation based on homomorphic encryption. The scenario is that a data holder encrypts a number of individual records, each of which consists of several phenotype and genotype data, and provide the encrypted data to an untrusted server. Then, the server performs a GWAS algorithm based on homomorphic encryption without the decryption key and outputs the result in encrypted state so that there is no information leakage on the sensitive data to the server. We develop a privacy-preserving semi-parallel GWAS algorithm by applying an approximate homomorphic encryption scheme HEAAN. Fisher scoring and semi-parallel GWAS algorithms are modified to be efficiently computed over homomorphically encrypted data with several optimization methodologies; substitute matrix inversion by an adjoint matrix, avoid computing a superfluous matrix of super-large size, and transform the algorithm into an approximate version. Our modified semi-parallel GWAS algorithm based on homomorphic encryption which achieves 128-bit security takes $30$--$40$ minutes for $245$ samples containing $10,000$--$15,000$ SNPs. Compared to the true $p$-value from the original semi-parallel GWAS algorithm, the $F_1$ score of our $p$-value result is over $0.99$.