2022/08/17 by Túlio Pascoal, Pascoal, Túlio, Jérémie Decouchant +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Cryptography and Security (cs.CR) #Distributed #Epigenetics and DNA Methylation #FOS: Biological sciences #FOS: Computer and information sciences #Genetic Associations and Epidemiology #Genetic Syndromes and Imprinting #Genomics (q-bio.GN) #Information Retrieval (cs.IR) #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2208.08361
openalex publication_date 2022/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.