使用基于CSI的同型加密启用深度学习框架安全跟踪患者的生命体征
IEEE journal of biomedical and health informatics
|August 22, 2025
概括
使用通道状态信息 (CSI) 和同态加密,VitalCrypt提供安全的实时生命信号监控. 这种保护隐私的框架在加密数据上实现了高精度的呼吸和心率分类.
科学领域:
- 数字健康
- 生物医学工程
- 网络安全
背景情况:
- 患者隐私是数字医疗的关键挑战.
- 非侵入性监控系统需要强大的数据安全性.
- 现有的方法往往难以平衡隐私与实时处理.
研究的目的:
- 推出VitalCrypt,一个新的安全,实时生命体征监测框架.
- 证明将通道状态信息 (CSI) 与同型加密和深度学习相结合的有效性.
- 解决医疗监测中的隐私保护解决方案的需求.
主要方法:
- 使用通道状态信息 (CSI) 进行非侵入性监控.
- 实现对加密数据进行直接计算的同态加密.
- 使用信号预处理 (汉普尔,萨维茨基-戈莱,圆过器,PCA) 和功率光谱密度 (PSD) 特性提取.
- 开发了一个具有加密兼容激活功能的轻量级神经网络用于分类.
主要成果:
- 在普通数据上达到99.46%的呼吸率分类准确度,在加密数据上达到99.44%的准确度.
- 尽管由于加密增加了运行时间,但显示的性能降低是微不足道的.
- 展示了有效的呼吸和心率分类,同时保持同型加密兼容性.
结论:
- 提供可扩展,保护隐私的实时医疗监测解决方案.
- 该框架平衡了高准确性和大量数据保密性.
- 它为远程监测和临床诊断中的安全深度学习应用提供了一个有希望的方法.
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