DyPS: Dynamic, Private and Secure GWAS (Summary) - GenoPri'21 Talk

Genome-Wide Association Studies (GWAS) identify the genomic variations that are statistically associated with a particular phenotype (e.g., a disease). GWAS results, i.e., statistics, benefit research and personalized medicine. The confidence in GWAS increases with the number of genomesanalyzed, which encourages federated computations where biocenters periodically include newly sequenced genomes. However, for legal and economical reasons, this collaboration can only happen if a release of GWAS results never jeopardizes the genomic privacy of data donors, if biocenters retain ownership and cannot learn each others’ data. Furthermore, given the reduced cost of sequencing DNA nowadays, there is now a need to update GWAS results in a dynamic manner, while enabling donors to withdraw consent at any time. Therefore, two challenges need to be simultaneously addressed to enable federated and dynamic GWAS: (i) the computation of GWAS statistics must rely on secure and privacy-preserving methods; and (ii) GWAS results that are publicly released should not allow any form of privacy attack. In this talk, we will introduce the problem we consider in more detail and present DyPS, the framework we have designed and recently presented at the Privacy Enhancing Technologies Symposium (PETS). We refer
the reader to the full paper1 for the details we cannot cover in this short version.