Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption
Name
s41467-021-25972-y.pdf
Description
Published version
Size
1.36 MB
Format
Adobe PDF
Checksum (MD5)
52c65e1cfc652b97fd133319279d0731
Author(s)
Froelicher, David
Troncoso-Pastoriza, Juan R
Raisaro, Jean Louis
Cuendet, Michel A
Sousa, Joao Sa
Cho, Hyunghoon
Berger, Bonnie
Fellay, Jacques
Hubaux, Jean-Pierre
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Froelicher, David, Troncoso-Pastoriza, Juan R, Raisaro, Jean Louis, Cuendet, Michel A, Sousa, Joao Sa et al. 2021. "Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractUsing 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.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-021-25972-Y
