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This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (1489) Ethics, Privacy, and Legal Issues (857) Tools, Programs and Algorithms (624) Big Data (451) Security and Privacy of mHealth and uHealth (221) Machine Learning (3404) Data Science (735)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/87306, first published .
Medical team in a meeting, discussing data on a large screen.

Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study

Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study

Authors of this article:

Yao Lu1 Author Orcid Image ;   Yu Tian2 Author Orcid Image ;   Tianshu Zhou1 Author Orcid Image ;   Jing Xia1 Author Orcid Image ;   Zhensong Zhao2 Author Orcid Image ;   Huiyao Sun1 Author Orcid Image ;   Kangli Liu1 Author Orcid Image ;   Jingsong Li2, 3 Author Orcid Image
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Journals

  1. Wang S, Jia X. Privacy protection of multi-source data combined with homomorphic encryption within a federated learning framework. Neural Computing and Applications 2026;38(20) View