  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.
