A Cloud-Assisted Medical Cyber-Physical System Using a Privacy-Preserving Key Agreement Framework and a Chebyshev Chaotic Map

At present, communication networks like the Internet of Things (IoT) are emerging with some of the latest technologies, such as artificial intelligence, big data, and cloud computing, to enable wireless edge infrastructures and seamless data migration. In particular, edge-based wireless communications help to gain new insights into the mitigation of potential communicable infections, e.g., COVID-19, via the use of IoT sensing devices. Of late, an evolving technology known as the cloud-assisted medical cyber-physical system (MCPS) has explored various key agreement protocols to examine security weaknesses between sensing devices and medical experts. Unfortunately, the existing schemes address vulnerabilities such as impersonation, privileged insider, and password guessing, whereby the behavior of the system becomes nondeterministic when guarding against malicious intent. Also, failures, faults, and attacks may vary in the characteristic forms of the IoT and the MCPS, causing unforeseen impairments in the system and for users. Thus, this article develops a privacy-preserving key agreement framework (PP-KAF) using the Chebyshev chaotic map mechanism to avoid privacy data disclosures and to protect session keys. The proposed PP-KAF exploits a strategy of two-way authentication not only to protect user identities but also to achieve untraceability from the remote server. Finally, a formal analytical model is applied to examine the properties of the key agreement protocol. Simulation results demonstrate that the proposed PP-KAF can offer better security efficiencies and can mitigate computation and communication overhead to guarantee improved quality metrics, namely, throughput rate and energy consumption.