Fast and Private Genomic Testing for Disease Susceptibility

Abstract

Advances in DNA sequencing are bringing mass computational genomic testing increasingly closer to reality. The sensitivity of genetic data, however, prompts the need for carefully protecting patients' privacy. Also, it is crucial to conceal the test's specifics, which often constitute a pharmaceutical company's trade secret. This paper presents two cryptographic protocols for privately assessing a patient's genetic susceptibility to a disease, computing a weighted average of patient's genetic markers (the "SNPs") and their importance factor. We build on the architecture introduced by Ayday et al. but point out an important limitation of their model, namely, that the protocol leaks which and how many SNPs are tested. Then, we demonstrate that an alternative SNP encoding can simplify (private) computations, and make patient-side computation on a smartcard device extremely efficient. A second protocol variant, based on secret sharing, further reduces online computation.