A data privacy protection method for infectious disease prediction models with balanced training speed and accuracy

Abstract

The application of deep learning technologies in constructing infectious disease prediction models has significantly enhanced public health strategies; however, the imperative for medical data privacy often prevents institutions from sharing diverse datasets, leading to data silos and diminished predictive accuracy. To address these challenges, we propose a multi-layered privacy-preserving framework that balances security and computational performance. First, we introduce a Random Transmission Hybrid Homomorphic algorithm that integrates CKKS fully homomorphic encryption with Paillier semi-homomorphic mechanisms, optimized by a random transmission sequence. Experimental evaluations demonstrate that this hybrid approach achieves a 25% improvement in computational and communication efficiency compared to conventional homomorphic encryption methods by reducing ciphertext overhead and skipping redundant update cycles. Second, we developed the Data Selection-Distributed Selection Stochastic Gradient Descent (DS-DSSGD) algorithm to optimize the trade-off between training speed and predictive accuracy. By filtering insignificant gradient updates and focusing on high-contribution features, the DS-DSSGD algorithm ensures high model precision even under the increased computational demands of privacy-preserving technologies. Finally, these innovations are integrated into the XDP Privacy Data Sharing Platform, providing a secure environment for end-to-end data lifecycle management. Collectively, our results indicate that the proposed framework not only safeguards sensitive health information but also maintains the high-precision forecasting capabilities essential for effective epidemic response.