Secure and distributed assessment of privacy-preserving GWAS releases

Túlio Pascoal, J.E.A.P. Decouchant, Marcus Völp

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 23rd conference on 23rd ACM/IFIP International Middleware Conference
Pages308-321
DOIs
Publication statusPublished - 2022
Event23rd conference on 23rd ACM/IFIP International Middleware Conference - Quebec City, Canada
Duration: 7 Nov 202211 Nov 2022
Conference number: 23

Conference

Conference23rd conference on 23rd ACM/IFIP International Middleware Conference
Abbreviated titleMiddleware '22
Country/TerritoryCanada
CityQuebec City
Period7/11/2211/11/22

Keywords

  • Federated GWAS
  • Privacy
  • Honest-but-curious
  • Collusion

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