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DyPS: Dynamic, Private and Secure GWAS (Summary)

Abstract : Genome-Wide Association Studies (GWAS) identify the genomic variations that are statistically associated with a particular phenotype (e.g., a disease). GWAS results, i.e., statistics, benefit research and personalized medicine. The confidence in GWAS increases with the number of genomes analyzed, which encourages federated computations where biocenters periodically include newly sequenced genomes. However, for legal and economical reasons, this collaboration can only happen if a release of GWAS results never jeopardizes the genomic privacy of data donors, if biocenters retain ownership and cannot learn each others' data. Therefore, two challenges need to be simultaneously addressed to enable federated GWAS: (i) the computation of GWAS statistics must rely on secure and privacy-preserving methods; and (ii) GWAS results that are publicly released should not allow any form of privacy attack. In this talk, we will introduce the problem we consider in more details and present DyPS [1], the framework we have designed and recently presented at the Privacy Enhancing Technologies Symposium (PETS). We refer the reader to the full paper 1 for the details we cannot cover in this short version.
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Contributor : Antoine Boutet Connect in order to contact the contributor
Submitted on : Sunday, September 26, 2021 - 10:25:06 PM
Last modification on : Friday, August 5, 2022 - 3:51:00 AM
Long-term archiving on: : Monday, December 27, 2021 - 6:11:17 PM


genopri_2021 (3).pdf
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  • HAL Id : hal-03354910, version 1


Túlio Pascoal, Jérémie Decouchant, Antoine Boutet, Paulo Esteves-Verissimo. DyPS: Dynamic, Private and Secure GWAS (Summary). GenoPri 2021 - 8th International Workshop on ​Genome Privacy and Security, Sep 2021, Online, France. pp.1-2. ⟨hal-03354910⟩



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