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Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption

Existing approaches to sharing of distributed medical data either provide only limited protection of patients’ privacy or sacrifice the accuracy of results. Here, the authors propose a federated analytics system, based on multiparty homomorphic encryption (MHE), to overcome these issues.

David Froelicher
Juan R. Troncoso-Pastoriza
Jean-Pierre Hubaux
ResearchOpen Access11 Oct 2021
Nature Communications

Volume: 12, P: 1-10
Author Correction: Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption

David Froelicher
Juan R. Troncoso-Pastoriza
Jean-Pierre Hubaux
Amendments and CorrectionsOpen Access11 Nov 2021
Nature Communications

Volume: 12, P: 1
Secure and federated genome-wide association studies for biobank-scale datasets

SF-GWAS is a workflow for secure, federated genome-wide association studies, implementing accurate, privacy-preserving principal-component analysis, linear/logistic regression and linear mixed model methods for biobank-scale multisite analyses.

Hyunghoon Cho
David Froelicher
Bonnie Berger
ResearchOpen Access24 Feb 2025
Nature Genetics

Volume: 57, P: 809-814

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