Efficient verification for outsourced genome-wide association studies.

With cloud computing is being widely adopted in conducting genome-wide association studies (GWAS), how to verify the integrity of outsourced GWAS computation remains to be accomplished. Here, we propose two novel algorithms to generate synthetic SNPs that are indistinguishable from real SNPs. The first method creates synthetic SNPs based on the phenotype vector, while the second approach creates synthetic SNPs based on real SNPs that are most similar to the phenotype vector. The time complexity of the first approach and the second approach is Om and Omlogn, respectively, where m is the number of subjects while n is the number of SNPs. Furthermore, through a game theoretic analysis, we demonstrate that it is possible to incentivize honest behavior by the server by coupling appropriate payoffs with randomized verification. We conduct extensive experiments of our proposed methods, and the results show that beyond a formal adversarial model, when only a few synthetic SNPs are generated and mixed into the real data they cannot be distinguished from the real SNPs even by a variety of predictive machine learning models. We demonstrate that the proposed approach can ensure that logistic regression for GWAS can be outsourced in an efficient and trustworthy way.