JMIR Publications

ABSTRACT

Background:

The development of robust medical artificial intelligence for knowledge discovery and decision support commonly necessitates large-scale datasets from multiple institutions. However, such data aggregation is severely constrained by privacy regulations and the inherent risk of sensitive information leakage, making it difficult to navigate the utility-privacy trade-off.

Objective:

We aimed to design a secure multi-party deep learning system that enables privacy-preserving modeling from distributed medical time-series data without centralizing raw information or exposing model parameters. Our goal was to achieve predictive accuracy comparable to non-secure models while providing strong security and efficiency.

Methods:

We developed a framework using threshold homomorphic encryption to securely train recurrent neural networks on distributed longitudinal data. To improve the efficiency, we proposed an optimized encrypted matrix multiplication scheme, a secure ciphertext refresh protocol, and employed lightweight encryption parameters and low-degree approximated activation polynomials. The system was evaluated on four real-world ICU datasets for tasks like mortality and sepsis prediction.

Results:

The system demonstrated practical efficiency, requiring approximately 1 minute per training iteration for processing 125 local batches over 39 variables and 48 time steps, and scaling well with data size and participant number. Securely trained models achieved predictive performance that was comparable to, and in some cases superior to, non-secure centralized models, highlighting its ability to learn generalizable patterns in different unseen data distributions. For example, on the PhysioNet challenge 2012 dataset, our secure model achieved AUC of 0.8480, outperforming the non-secure baseline AUC of 0.8404.

Conclusions:

This work provides a viable and efficient solution for cross-institutional, privacy-preserving analysis of longitudinal medical data. The framework successfully bridges the utility-privacy gap, facilitating safer collaborative research and enabling robust knowledge discovery and decision support while adhering to strict data protection standards.