I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies
Túlio Pascoal (Université du Luxembourg)
Jérémie Decouchant (TU Delft - Data-Intensive Systems)
Antoine Boutet (ENS-PSL Research University & CNRS, INSA Lyon)
Marcus Völp (University of Luxembourg)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAScontinuously releases privacy-preserving and noise-free GWAS results as new genomes become available.
help_outline