Quantum-Enhanced Protection Of Healthcare Data In Medical Cyber-Physical Systems With Deep Convolutional Neural Networks

Abstract

 The advancements in quantum computing and deep learning algorithms may help establish a suitable technique to safeguard the health information stored in MCPSs against cyber risks. Based on this background, the following is a research study recommendation that will assist in evaluating and investigating the application of quantum computing and DCNNs for securing MCPS healthcare data. Considering the growing potential of such damaging cyber threats, discussing the necessity of security would be essential. We then briefly give an overview of quantum computing and DCNN's security capabilities. The following section proposes an innovative architecture incorporating QKD for secure networking and DCNNs to detect oddities and threats in MCPS. Here are four essential management and exchange systems relevant to using quantum ideas to authenticate and protect health records. Furthermore, this paper presents a lightweight mutual identification establishment algorithm capable of improving access control and authorization within MCPS. To justify the efficiency of the present design, we simulated the system and compared our proposed solution with the benchmarks to show the improvement in data security and threat detection. Therefore, our study also shows that improving the security of MCPS for healthcare systems is feasible by applying QE and increasing the structures' resistance in healthcare networks.