Octopus: A Novel Approach for Health Data Masking and Retrieving using Physically Unclonable Function and Machine Learning

The health equipment is 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 all devices are linked to high-speed internet and 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 proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been 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.本类医疗设备用于监测关键健康指标、自动化执行健康干预措施并开展健康指标分析。随着各类设备与高速互联网及移动终端实现互联互通，人们已开始借助移动应用程序追踪自身健康特征与医疗需求。智能设备、互联网与移动应用的这种融合，拓展了医疗物联网（Internet of Medical Things, IoMT）在远程健康监测领域的应用场景。医疗物联网的开放性与不可预测性特征，会为其系统带来严重的安全与机密性威胁。在本研究论文中，研究团队采用Octopus方案与物理不可克隆函数（Physically Unclonable Functions, PUFs），通过数据掩码为医疗设备提供隐私保护，并结合机器学习（Machine Learning, ML）技术实现健康数据的恢复，同时降低网络层面的安全漏洞风险。该技术的准确率可达99.45%，证实了基于数据掩码的该技术可有效保障健康数据的安全。