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.
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.
Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioners—This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data communication, and the prediction of malicious entities. We have developed a mathematical system to portray the overall management of healthcare data. All techniques proposed in this paper have been implemented using quantum computing and have been tested on four healthcare datasets. Initial experimental results suggest that the proposed approach is feasible. Our techniques can be applied to discover malicious entities and understand the behavior of real-life users in healthcare processes.
The healthcare sector increasingly relies on Cyber-Physical Systems (CPS) that integrate medical devices, networks, and digital records to deliver critical patient care. Protecting sensitive medical data within these interconnected systems is vital due to potential privacy violations and safety risks. The initiation of quantum computing poses substantial threats to traditional cryptographic algorithms, which underpin current healthcare data security. This article examines the deployment of quantum-safe cryptographic algorithms considered to resist quantum attacks, enabling robust protection of healthcare CPS. It highlights advanced encryption methods including lattice-based, hash-based, and code-based post-quantum schemes, alongside quantum key distribution (QKD) for secure key management. The discussion includes challenges related to healthcare-specific system constraints, regulatory compliance, and integration complexity. Through adopting quantum-resistant cryptography and comprehensive key management protocols, healthcare organizations can safeguard electronic health records (EHRs), device communication, and patient data against incipient post-quantum threats, ensuring long-term data confidentiality, integrity, and system resilience.
The integration of blockchain technology in electronic medical records (EMRs) offers enhanced data security and integrity. However, classical blockchain systems face potential threats from quantum computing and often involve significant computation and communication costs. This paper proposes a secure framework for safeguarding EMRs using quantum blockchain technology, leveraging quantum cryptographic principles to enhance security. The proposed framework employs Quantum Key Distribution (QKD) and post-quantum cryptographic algorithms to resist quantum attacks, ensuring robust protection of sensitive data. By optimizing consensus mechanisms with quantum algorithms, with this research work, a 30% reduction in computation costs and a 25% decrease in communication overhead is achieved compared to traditional blockchain systems. These improvements are quantified through simulation results, demonstrating the framework’s efficiency and practicality. The proposed solution ensures higher security, integrity, and cost-effectiveness for managing EMRs in the face of emerging quantum threats.
Applying quantum artificial intelligence (QAI) to protect healthcare systems includes several technologies that improve data security and protect against threats. The chapter emphasizes the need for advanced cybersecurity techniques in healthcare, integrating QAI to protect sensitive data. Traditional defense frameworks are insufficient, and QAI, a combination of quantum computing and artificial intelligence (AI), is needed to address the complexities of cyber threats and establish enduring cybersecurity frameworks. The chapter describes the cybersecurity problems of the healthcare sector. Digital transformation has revolutionized the healthcare sector by introducing electronic health records, connected medical devices, and telemedicine which now provide a bigger target for cyber adversaries. The delicacy of health data is the reason why it is the major target of ransomware, data breaches, and other hacking techniques. As the threats grow more complicated, traditional security solutions may become less effective. This chapter presents QAI as a novel technique for taking care of these issues using the incredible computational power of quantum computing combined with the pattern recognition and decision-making skills of AI. Furthermore, the chapter reviews the field of quantum resistance, which is defined as the capability of algorithms to deliver secure encryption even in the face of quantum computers; under this context, the chapter presents lattice-based and hash-based cryptography as critical to protecting healthcare data from future quantum cyberattacks. These encryption techniques protect against threats from quantum computing and most of today’s common encryption methods such as the Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are vulnerable to breakage by quantum machines.
Quantum computing represents a major threat to future communication systems security: Once a certain level of maturity is reached, the security enjoyed by classical cryptographic schemes will immediately be put in check by (i) highly efficient quantum algorithms able to solve hard problems non-negligibly faster than the best-known classical solutions and (ii) potentially scalable fabrication techniques for quantum circuits. This work investigates the design of quantum-safe security primitives suitable for new cybersecurity threats in next-generation communications networks by means of the integrated use of quantum cryptography and deep learning. By combining the unconditionally secure guarantees offered by quantum cryptographic protocols with the adaptive, data-driven intelligence of deep learning models, this work proposes a holistic solution to secure communication infrastructures against not only current but also future cyber threats. A central element is the utilization of QKD-protocols that use the properties of quantum mechanics in order to securely distribute keys that are resistant to computational attacks. In parallel thereof, deep learning techniques are applied to monitor the system by analyzing real-time network traffic, identifying abnormal activities, and forecasting sophisticated attack patterns that frequently are overlooked by conventional security systems. This approach mitigates constraints of standalone quantum cryptography, including noise and hardware imperfections, and improves threat identification over traditional signature-based systems. The work studies the synergy between quantum cryptography and AI-assisted analytics, optimizing quantum channel estimation, error correction, and intrusion detection with deep learning. We conduct extensive simulations to show the efficacy of the framework against various adversarial attacks such as man in the middle, side-channel, and adversarial machine learning threats. The system has high stability without loss of quantum bit error rate, and high detection accuracy, for handling the change of attack vectors in distributed, heterogeneous communication networks.
The rapid advancement of quantum computing poses a significant threat to current cryptographic standards, particularly those securing electronic health records (EHRs) in healthcare systems.Traditional encryption methods such as RSA, ECC, and AES, while robust against classical attacks, are vulnerable to quantum algorithms like Shor's and Grover's, which can efficiently break cryptographic keys and compromise patient data integrity.As healthcare systems become increasingly interconnected through the Internet of Things (IoT) and cloud-based infrastructures, the need for quantum-resistant security mechanisms becomes paramount.Post-Quantum Cryptography (PQC) emerges as a viable solution to future-proof EHR security by employing lattice-based, hash-based, code-based, and multivariate polynomial cryptographic techniques that remain resistant to quantum attacks.This paper explores the implications of quantum computing on healthcare cybersecurity, emphasizing the vulnerabilities of existing encryption protocols and the necessity of transitioning toward PQC frameworks.Furthermore, we examine the integration of PQC within healthcare infrastructures, focusing on key exchange mechanisms, digital signatures, and end-to-end encryption for secure EHR transactions.Additionally, the study highlights regulatory and compliance considerations, including the National Institute of Standards and Technology (NIST) PQC standardization efforts and the role of Zero Trust security models in mitigating quantum-era cyber risks.Case studies illustrate successful implementations of PQC in healthcare networks, demonstrating its feasibility and effectiveness in safeguarding sensitive medical information.By adopting quantum-resistant cryptographic standards, healthcare institutions can proactively defend against emerging cyber threats, ensuring data confidentiality, integrity, and long-term resilience in the face of quantum computing advancements.