Privacy-Preserving Strategies in the Internet of Medical Things Using Reinforcement Learning and Blockchain

The advancement of the Internet of Things has markedly influenced the development of the Internet of Medical Things, enabling healthcare services to manage substantial amounts of sensitive private data. However, the increased generation of data raises critical concerns regarding the security and privacy of information transmitted within these systems. Current traditional security solutions are not designed to dynamically adapt to the evolving nature of cyber threats and, therefore, do not adequately meet the modern requirements of healthcare. In this paper, we propose a security framework based on blockchain technology and distributed reinforcement learning to effectively address these vulnerabilities. A decentralized cognitive blockchain network is utilized to ensure that data are stored securely and transmitted reliably while minimizing resource utilization. Furthermore, distributed reinforcement learning is integrated to enable security measures to adapt to changing threat patterns and enhance system resilience against attacks. Performance evaluations using the Address Resolution Protocol man-in-the-middle and Mirai botnet datasets demonstrated the framework’s ability to enhance Internet of Medical Things security compared to the ability of existing methods, resulting in memory consumption and transaction latency, while maintaining high data throughput. By bolstering the security of medical cyber-physical systems against various threats, this approach offers a promising solution to enhance operational efficiency and optimize the security of medical data in an interconnected environment.