安全的联合传输学习与增强的安全多方计算,以保护隐私,保护智能EHR系统
Tae Hoon Kim1, C Rohith Bhat2, Temesgen Engida Yimer3
1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, No. 318, Hangzhou, Zhejiang, China.
Scientific reports
|December 19, 2025
概括
本研究引入了一个安全联合传输学习 (SFTL) 框架与安全多方计算 (SMPC) 以保护智能电子健康记录 (EHR) 中的患者数据. SFTL-SMPC方法增强了隐私,同时使医疗机构的协作AI模型培训成为可能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
背景情况:
- 电子健康记录 (EHR) 对个性化医疗保健至关重要,但也引发了重大隐私和安全问题.
- 由于隐私要求和数据孤岛,跨机构共享敏感患者数据具有挑战性.
- 联合学习 (FL) 和人工智能 (AI) 提供了医疗保健进步的潜力,但需要强有力的隐私保护方法.
研究的目的:
- 为智能EHR系统提出一个与安全多方计算 (SMPC) 集成的安全联合传输学习 (SFTL) 框架.
- 在分布式患者数据的协作分析中解决关键的隐私和安全挑战.
- 为了使医疗保健提供者能够在不影响个体患者隐私的情况下对分散的EHR数据进行机器学习模型的训练.
主要方法:
- 开发一个安全的联合传输学习 (SFTL) 架构.
- 集成安全多方计算 (SMPC) 用于维护隐私的统计计算和模型参数聚合.
- 使用现实世界智能EHR数据集对SFTL-SMPC框架的评估.
主要成果:
- SFTL-SMPC框架有效地平衡了数据隐私和模型准确性.
- 与传统的联合学习方法相比,拟议的方法显示出更高的性能.
- 在全面分析中,SFTL-SMPC的实施显示了对各种安全威胁的弹性.
结论:
- SFTL-SMPC框架为智能EHR系统中的保护隐私的协作学习提供了安全有效的解决方案.
- 这种方法促进了人工智能在医疗保健中的进步,因为它使数据的安全利用成为可能.
- 该研究验证了SFTL-SMPC架构对于敏感健康数据分析的稳定性和有效性.
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