A Privacy-Preserving Secure Data Processing Scheme for the Internet of Medical Things
Ying Huang, Yongmei Su · Electronics ETF · 2024
The Internet of Medical Things (IoMT) offers diverse application support through monitoring, analysis, and recommendations. This application paradigm relies on sensitive internal and external data to meet user needs. This article introduces a secure data processing scheme for leveraging the IoMT application performance. This scheme is named Persuaded Data Processing with Digital Security (PDP-DS), ensuring user and data privacy. This scheme focuses on IoMT-aided remote monitoring application security where data openness is high and false data chances are high. During the application support, the blockchain concept is applied for data authentication. In this scheme, user-verified digital signatures are used for authentication. This scheme provides data processing recommendations based on the deep learning paradigm. This output is authenticated alone using a password/ PIN-based digital signature. The learning process identifies the processing required and security-filtered instances using the recursive states identified. The proposed scheme ensures fewer false data processing based on its attributes and recommendation factor. PDP-PS reduces the considered metrics to 9.95% (for process delay), 10.19% (for service delay), 10.6% (for replication factor), 10.44% (for false rate), and 7.39% (for backlogs).