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


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 $0.99$.

Available format(s)
Publication info
Preprint. MINOR revision.
homomorphic encryptionGWASFisher scoringprivacyapproximate computation
Contact author(s)
doodoo1204 @ snu ac kr
2019-02-20: received
Short URL
Creative Commons Attribution


      author = {Duhyeong Kim and Yongha Son and Dongwoo Kim and Andrey Kim and Seungwan Hong and Jung Hee Cheon},
      title = {Privacy-preserving Approximate GWAS computation based on Homomorphic Encryption},
      howpublished = {Cryptology ePrint Archive, Paper 2019/152},
      year = {2019},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.