  Genome-wide association studies (GWAS) identify correlations between the
genetic variants and an observable characteristic such as a disease. Previous
works presented privacy-preserving distributed algorithms for a federation of
genome data holders that spans multiple institutional and legislative domains
to securely compute GWAS results. However, these algorithms have limited
applicability, since they still require a centralized instance to decide
whether GWAS results can be safely disclosed, which is in violation to privacy
regulations, such as GDPR. In this work, we introduce GenDPR, a distributed
middleware that leverages Trusted Execution Environments (TEEs) to securely
determine a subset of the potential GWAS statistics that can be safely
released. GenDPR achieves the same accuracy as centralized solutions, but
requires transferring significantly less data because TEEs only exchange
intermediary results but no genomes. Additionally, GenDPR can be configured to
tolerate all-but-one honest-but-curious federation members colluding with the
aim to expose genomes of correct members.
