Efficient Collaborative Model Training Mechanism With Privacy-Preserving Data for the IoMT

As time-series data from the Internet of Medical Things (IoMT) increasingly permeates various aspects of medical research, public governance, and clinical treatment, its sensitivity raises significant privacy concerns, hindering the potential of deep learning applications for cross-institutional data integration. Previous practices focused on deep learning methods based on centralized data storage and processing, which are often unsuitable for decentralized and privacy-sensitive IoMT data scenarios. Most existing methods rely on mechanisms such as trusted coordinators, which face challenges in addressing potential passive data leakage and side-channel attacks, failing to effectively protect the privacy of sensitive data during collaborative training. To address these issues, we propose a privacy-preserving collaborative training model, Secure Long Sequence Time-Series Forecasting (SecLSTF), for IoMT time-series data and design a mapping strategy between model components and Multi-Party Computation (MPC) protocols. Building on this foundation, we propose a novel secret sharing protocol, Pleione, which focuses on optimizing the computational efficiency of the low-level secret-sharing protocol. The protocol centers on a hyper-invertible matrix and adopts a paired double random expansion mechanism, significantly reducing the communication rounds required for random number generation. This optimization enhances the overall training speed of SecLSTF. Subsequently, we replace the original computational support protocol with Pleione. Experimental results show that SecLSTF-Pleione significantly reduces computational time while maintaining computational accuracy, outperforming other protocols in component efficiency. This study offers a potential pathway for cross-institutional IoMT data sharing.