Privacy Preserving Based on Seamless Authentication With Provable Key Verification Using mIoMT for B5G-Enabled Healthcare Systems

B5G-enabled healthcare systems interconnect a wide range of Internet of Medical Things (IoMT) using supportive networks such as heterogeneous networks and cognitive radio networks to enhance the medical infrastructure. In healthcare, IoMT integrates access technologies, computing infrastructure, and services to connect healthcare systems to handle intensive computation without sharing private data. As a result, healthcare systems accessing a massive IoMT (mIoMT) utilize real-time data sharing to enhance the overall resource efficiency of remote patient monitoring. To optimize the IoT-generated data, the application interface of the computing device regulates self-management messaging systems with healthcare providers. By utilizing direct communication with the networks, they offer a long-lasting service, enhancing the performance trade-off. Since the network has more of a digital existence in the physical universe, a convergence of cloud-server integration with IoT inherently causes more security challenges to preserving the privacy of edge computing systems. Therefore, in this paper, we present privacy preserving based seamless authentication with provable key verification (PPSA-PKV) for securing B5G-enabled healthcare systems. To preserve the identities of the registered users, the proposed PPSA-PKV applies a collision-free cryptographic hash function and elliptic-curve arithmetic. Security analyses including formal and informal show high-level privacy protection for the proposed PPSA-PKV with seamless verification compared to other state-of-the-art approaches. The simulation analysis shows that the proposed PPSA-PKV incurs less delay ( <tex-math notation="LaTeX">≈ 0.14 sec</tex-math> ) and improves throughput ( <tex-math notation="LaTeX">≈ 1865 bits</tex-math> ) to fulfill the energy efficiency (at an average 0.294J) of B5G networks. Lastly, a learning model using a support vector machine (SVM) demonstrates the monitoring process of edge data centers to detect malicious authentication requests.