Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.
Details
Serval ID
serval:BIB_3683F2C63090
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.
Journal
Nature communications
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
11/10/2021
Peer-reviewed
Oui
Volume
12
Number
1
Pages
5910
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Publication Status: epublish
Abstract
Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations.
Keywords
Algorithms, Computer Security, Delivery of Health Care, Genome-Wide Association Study, Humans, Kaplan-Meier Estimate, Precision Medicine, Privacy, Survival Analysis
Pubmed
Web of science
Open Access
Yes
Create date
19/10/2021 11:25
Last modification date
21/11/2022 8:21