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.
Data availability
The data that support the findings of this study are available from [BaseBit] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [BaseBit].
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Funding
This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2024ZD0532900), the National Natural Science Foundation of China (No. 62372316), and the Sichuan Science and Technology Program Key Project (Nos. 2024YFHZ0091, 2025YFHZ0066).
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Xinhang Wang: investigation, experiment and writing; Yuncheng Jiang: writing and revision; Guangming Pan: validation, investigation and data collection; Zhen Luo: Supervision and data collection; Ming Xiao: supervision; Li Yang: supervision; Xiaoqiu Shi: supervision; Ying Huo: supervision; Mianyang Li: supervision; Le Zhang (Corresponding Author): conceptualization, supervision and writing.
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Wang, X., Jiang, Y., Pan, G. et al. A data privacy protection method for infectious disease prediction models with balanced training speed and accuracy. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38906-9
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DOI: https://doi.org/10.1038/s41598-026-38906-9
