A Privacy-Preserving GWAS Computation with Homomorphic Encryption

The continuous decline in the cost of DNA sequencing has contributed bothpositive and negative feelings in the academia and research community. It hasnow become possible to harvest large amounts of genetic data, which researches believe their study will help improve preventive and personalised healthcare, better understanding of diseases and response to treatments. However, there are more information embedded in genes than are currently understood, just as a genomic data contains information of not just the owner, but relatives who might not subscribe to sharing them. Unrestricted access to genomic data can be privacy invasive, hence the urgent need to regulate access to them and develop protocols that would allow privacy-preserving techniques in both computations and analysis that involve these very sensitive data. In this work, we discuss how a careful combination of cryptographic primitives such as homomorphic encryption, can be used to privately implement common algorithms peculiar to genome-wide association studies (GWAS). This obviously comes at a cost, where we have to accommodate the trade-off between speed of computations and privacy.Cyber SecurityIntelligent System