Lightweight secure communication framework with eXplainable artificial intelligence for trustworthy healthcare analytics

The Internet of Medical Things (IoMT) allows seamless integration across healthcare information systems. It facilitates communication among remote healthcare devices, enabling rapid and flexible analysis of healthcare data. This, in turn, enhances overall digital healthcare outcomes. However, the inherent vulnerabilities of IoMT devices and the sensitive nature of healthcare data pose significant security and privacy risks. To address these challenges, we propose a lightweight secure communication framework for IoMT by employing efficient cryptographic primitives, including hash functions, XOR and AES-CBC-256, in conjunction with a Physical Unclonable Function (PUF), to enhance authentication and key exchange as well as to strengthen resistance against physical attacks and tampering. Moreover, the integration of eXplainable Artificial Intelligence (XAI) techniques ensures trust in healthcare data analytics. To demonstrate the effectiveness of the proposed framework in thwarting a variety of attacks, we conduct a security analysis using both informal analysis and the formal real-or-random model. Our comparative analysis confirms that our framework surpasses other existing schemes, in terms of computation and communication overheads as well as security and functionality features. By prioritizing security, privacy, and transparency, our framework contributes to the development of a secure and trustworthy IoMT ecosystem, enabling the full potential of digital healthcare.