在医疗事物互联网中使用同型加密的安全医疗建议的联合学习方法
IEEE journal of biomedical and health informatics
|January 8, 2024
概括
联合学习 (FL) 通过使用同态密码学来保护用户梯度来增强医疗推系统. 这种方法显著改善了用户隐私,对推准确性的影响最小.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 联合学习 (FL) 在边缘设备上训练模型,保持医疗物联网 (IoMT) 设备上的数据完整性.
- FL的主要挑战是由于培训期间可能发生梯度泄漏,用户隐私不完全.
- 现有的FL方法难以完全保护传送到全球服务器的用户数据.
研究的目的:
- 提出一个新的联合医疗推系统,以加强用户隐私.
- 为了解决医学应用的联合学习中的梯度泄漏问题.
- 为了利用同态密码学来实现安全的梯度传输和计算.
主要方法:
- 总结联合学习过程.
- 使用同型密码学开发一个联合的医疗推系统.
- 实施联合随机修改最小化梯度 (FSMMG) 算法.
- 进行实验,以评估隐私保护和建议准确性.
主要成果:
- 拟议的系统实现了更高的用户隐私百分比,而准确性却略有下降.
- 对加密梯度的计算对推输出的影响最小,确保梯度传输的安全通道.
- FSMMG算法显示了性能改善和与越来越多的用户的良好融合.
- 与现有技术相比,FSMMG表现出卓越的性能,实现98.3%的加密精度.
结论:
- 同型密码学有效地提高了联合医疗推系统中的用户隐私.
- 拟议的FSMMG算法为医疗保健中的隐私保护联合学习提供了安全有效的解决方案.
- 这种方法平衡了对准确建议的需求和强大的用户数据保护.
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