Towards practical privacy-preserving genome-wide association study

By Charlotte Bonte, Eleftheria Makri, A. Ardeshirdavani, Jaak Simm, Yves Moreau, Frederik Vercauteren. 2018

35 citations recorded by the source corpus.

The deployment of Genome-wide association studies (GWASs) requires genomic information of a large population to produce reliable results. This raises significant privacy concerns, making people hesitate to contribute their genetic information to such studies. We propose two provably secure solutions to address this challenge: (1) a somewhat homomorphic encryption (HE) approach, and (2) a secure multiparty computation (MPC) approach. Unlike previous work, our approach does not rely on adding noise to the input data, nor does it reveal any information about the patients. Our protocols aim to prevent data breaches by calculating the χ2 statistic in a privacy-preserving manner, without revealing any information other than whether the statistic is significant or not. Specifically, our protocols compute the χ2 statistic, but only return a yes/no answer, indicating significance. By not revealing the statistic value itself but only the significance, our approach thwarts attacks exploiting statistic values. We significantly increased the efficiency of our HE protocols by introducing a new masking technique to perform the secure comparison that is necessary for determining significance. We show that full-scale privacy-preserving GWAS is practical, as long as the statistics can be computed by low degree polynomials. Our implementations demonstrated that both approaches are efficient. The secure multiparty computation technique completes its execution in approximately 2 ms for data contributed by one million subjects.

Subfields: Artificial Intelligence, Genetics

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The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “It is also of core interest in genome-wide association studies (GWAS), where the inputs are genomes of patients a ﬀ ected by a particular disease and the goal is to (approximately) identify disease-related genome locations and estimate their signiﬁcance [3, 67, 15].”