机器学习加密方法用于医疗保健中的物联网
Tserendorj Chinbat1, Samaneh Madanian2, David Airehrour3
1Department of Computer Science and Software Engineering, Auckland University of Technology (AUT), 6 St. Paul Street, Auckland, 1010, New Zealand.
BMC medical informatics and decision making
|June 3, 2024
概括
这项研究评估了轻量加密 (LWC) 算法,用于在医疗保健物联网 (IoT) 设备中保护患者数据. RECTANGLE算法表现出卓越的性能,使其成为增强数据隐私和安全的理想选择.
科学领域:
- 网络安全 网络安全
- 应用密码学 应用密码学
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 越来越多的人在医疗保健中使用物联网 (IoT),这引发了严重的患者数据安全和隐私问题.
- 轻量加密 (LWC) 算法为在资源有限的物联网设备上保护敏感健康信息提供了有希望的解决方案.
研究的目的:
- 确定最适合和最有效的轻量级加密 (LWC) 算法,以在医疗保健物联网环境中保护患者数据.
- 根据与物联网应用相关的关键指标评估和比较各种LWC算法的性能.
主要方法:
- 使用机器学习模型对八个LWC算法 (AES,PRESENT,MSEA,LEA,XTEA,SIMON,PRINCE,RECTANGLE) 的性能评估.
- 在Raspberry Pi 3微控制器上进行的实验具有不同的文件大小 (16KB到2048KB).
- 性能指标包括精度,回忆,F1分数,准确性,加密/解密时间,能源消耗,内存使用量和吞吐量.
主要成果:
- RECTANGLE算法被确定为医疗保健物联网应用程序中最有效和最适合的LWC算法.
- 与其他评估算法相比,RECTANGLE在速度,效率,简单性和灵活性方面表现出色.
- 分析为每个算法提供了关于加密/解密速度,能源使用,内存足迹和数据吞吐量的全面数据.
结论:
- 该研究成功地解决了医疗保健物联网系统中关键的安全和隐私挑战.
- 在这种情况下,使用系统文献审查 (SLR) 方法识别了LWC算法的关键性能因素.
- 这些发现为选择最佳的LWC算法提供了宝贵的见解,以加强医疗保健物联网部署中的隐私和安全性.
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