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

TL;DR

I-GWAS is a framework that enables secure, continuous, and privacy-preserving release of GWAS results even when multiple studies share overlapping participants and data, addressing vulnerabilities in existing methods.

Contribution

The paper introduces I-GWAS, a novel framework that securely computes and releases interdependent GWAS results without compromising privacy.

Findings

Existing methods are vulnerable to privacy attacks with overlapping datasets.

I-GWAS effectively prevents genomic reconstruction and membership inference attacks.

It supports continuous, noise-free updates as new data arrives.

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.