Secure and distributed assessment of privacy-preserving GWAS releases

Secure and distributed assessment of privacy-preserving GWAS releases

Pascoal, Túlio (University of Luxembourg)
Decouchant, Jérémie (TU Delft Dataintensive Systems)
Völp, Marcus (University of Luxembourg)

2022

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 operate on the data and decide whether GWAS results can be safely disclosed, which violates 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.

Federated GWAS
Privacy
Honest-but-curious
Collusion

http://resolver.tudelft.nl/uuid:ca512d5a-2ef9-4216-a928-76de93e8a871

https://doi.org/10.1145/3528535.3565253

Proceedings of the 23rd conference on 23rd ACM/IFIP International Middleware Conference

23rd conference on 23rd ACM/IFIP International Middleware Conference, 2022-11-07 → 2022-11-11, Quebec City, Canada

Institutional Repository

conference paper

© 2022 Túlio Pascoal, Jérémie Decouchant, Marcus Völp