Open access
Aug 2026
Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study
This work designs a secure multiparty deep learning system that enables privacy-preserving modeling from distributed medical time-series data without centralizing raw information or exposing model parameters, and successfully bridges the utility-privacy gap.
Yao Lu, Yu Tian, Tianshu Zhou et al.
· JMIR Formative Research · 0 citations