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Conference Papers Year : 2023

I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies

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-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.
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Dates and versions

hal-03781755 , version 1 (20-09-2022)

Identifiers

  • HAL Id : hal-03781755 , version 1

Cite

Túlio Pascoal, Jérémie Decouchant, Antoine Boutet, Marcus Völp. I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies. PETS 2023 - 23rd Privacy Enhancing Technologies Symposium, Jul 2023, Lausanne, Switzerland. pp.1-17. ⟨hal-03781755⟩
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