College of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48858, USA.
Department of Mathematics and Computer Science, Texas Woman's University, Denton, TX 76204, USA.
Sensors (Basel). 2023 Apr 18;23(8):4082. doi: 10.3390/s23084082.
Health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because devices are now linked to high-speed internet and mobile phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this paper, Octopus and Physically Unclonable Functions (PUFs) are used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.
健康设备用于跟踪重要的健康指标、自动化健康干预措施以及分析健康指标。由于现在设备已经连接到高速互联网和移动电话，人们开始使用移动应用程序来跟踪健康特征和医疗需求。这种智能设备、互联网和移动应用程序的结合通过医疗物联网（IoMT）扩展了远程健康监测的使用。IoMT 的可访问性和不可预测性在 IoMT 系统中造成了巨大的安全和机密性威胁。在本文中，八爪鱼和物理不可克隆功能（PUF）被用来通过屏蔽数据为医疗保健设备提供隐私，并使用机器学习（ML）技术检索健康数据并减少网络安全漏洞。该技术表现出 99.45%的准确率，这证明该技术可用于通过屏蔽来保护健康数据。
