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

Author

Listed:
  • David Froelicher

    (EPFL)

  • Juan R. Troncoso-Pastoriza

    (EPFL)

  • Jean Louis Raisaro

    (Lausanne University Hospital
    Lausanne University Hospital)

  • Michel A. Cuendet

    (Lausanne University Hospital)

  • Joao Sa Sousa

    (EPFL)

  • Hyunghoon Cho

    (Broad Institute of MIT and Harvard)

  • Bonnie Berger

    (Broad Institute of MIT and Harvard
    MIT
    MIT)

  • Jacques Fellay

    (Lausanne University Hospital
    EPFL)

  • Jean-Pierre Hubaux

    (EPFL)

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.

Suggested Citation

  • David Froelicher & Juan R. Troncoso-Pastoriza & Jean Louis Raisaro & Michel A. Cuendet & Joao Sa Sousa & Hyunghoon Cho & Bonnie Berger & Jacques Fellay & Jean-Pierre Hubaux, 2021. "Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25972-y
    DOI: 10.1038/s41467-021-25972-y
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    Cited by:

    1. Tao Qi & Fangzhao Wu & Chuhan Wu & Liang He & Yongfeng Huang & Xing Xie, 2023. "Differentially private knowledge transfer for federated learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Miran Kim & Xiaoqian Jiang & Kristin Lauter & Elkhan Ismayilzada & Shayan Shams, 2022. "Secure human action recognition by encrypted neural network inference," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Helin Yang & Kwok-Yan Lam & Liang Xiao & Zehui Xiong & Hao Hu & Dusit Niyato & H. Vincent Poor, 2022. "Lead federated neuromorphic learning for wireless edge artificial intelligence," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Chongliang Luo & Md. Nazmul Islam & Natalie E. Sheils & John Buresh & Jenna Reps & Martijn J. Schuemie & Patrick B. Ryan & Mackenzie Edmondson & Rui Duan & Jiayi Tong & Arielle Marks-Anglin & Jiang Bi, 2022. "DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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