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.
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.
Greater digitization of healthcare institutions demands secure mechanisms of protecting sensitive medical information from unauthorized access and violations. This paper introduces QuantumShield, a new framework combining quantum cryptography and predictive analytics to provide enhanced security in handling healthcare data. QuantumShield employs quantum one-time pad encryption (QOTPE) for un-hackable storage and data transmission, supplemented by a quantum-enabled neural network (QENN) for real-time detection of malicious access attempts. Through the application of quantum principles, the system provides unmatched security and predictive performance. Experiments on a range of healthcare datasets show that QuantumShield achieves 92% accuracy in detecting malicious entities and lowering encryption overhead by 15% over traditional methods. The paper presents an extensible, quantum-inspired method for ensuring healthcare data confidentiality and integrity in distributed settings, a new paradigm in secure medical data processing.
The rising cases of cyber threats to sensitive government and healthcare information has forced newer and stronger systems of data protection. Conventional encryption protocols, though efficient, are losing their effectiveness due to the enhanced cyber-attacks especially by foreign enemies. The paper examines how quantum enhanced, privacy aware Artificial Intelligence (AI) systems, whose framework is supported by physical concepts, can be used to protect important data. We suggest a combination of quantum computing as a way to achieve increased security and AI-based, privacy-protective methods, including federated learning and blockchain. The quantum nature makes sure the data is encrypted and computed in a manner that cannot be easily broken using the common cryptographic mechanisms, and AI makes the most out of the situation and adjusts to evolving threats on the fly. By building these structures, we would like to develop a model that will easily prevent unauthorized access, and address the risks posed by external cyber threats. The possible uses of these quantum-enhanced AI systems in governmental and healthcare data are also discussed in the paper, providing the effectiveness of such systems in not only sharing data safely but also predicting a risk in real time. The new method offers a bright way out of the current fight against the advanced cyber attacks of the sensitive information.
The rapid evolution of quantum computing puts traditional encryption techniques at risk of becoming ineffective, potentially exposing sensitive healthcare information to unprecedented cyber vulnerabilities. This research proposes the implementation of quantum-driven cryptographic framework, which utilizes quantum key distribution, quantum-safe algorithms, and random high entropy quantum number generators to ensure the safety of healthcare data on different health information systems. This framework advocates for the unification of EHRs, telemedicine, and IoT-enabled devices. This incorporation defends not only against current cyber vulnerabilities, but also future sequential cyber risks. This is crucial in protecting patient trust within the healthcare system. The goal is to develop a protected healthcare crypto framework best described as box-quantum safe, scalable and dynamically compliant with respective health laws cryptographically enclosing the sensitive data against relational and quantum attacks. The framework prioritizes patient data accessibility with seamless security with the incorporation of quantum key distribution, quantum safe algorithms, and entropy quantum number generators. The framework also facilitates uninterrupted incorporation with EHRs, telemedicine and connected devices while assuring continuous compliance with prevailing data protection legislations. The quantum driven frameworks to cryptographic systems are highly beneficial in safeguarding sensitive data, as it offers robust protection from current and emerging cyber threats. Integrating qsafe encryption and modern healthcare data management systems enables the proposed system to safeguard the patient's privacy, anger no international security compliance issues, and be resilient to 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.
The rapid growth of Internet of Things (IoT)-enabled healthcare systems has led to the widespread use of cloud-based platforms for storing and transmitting medical images. While this paradigm improves accessibility and scalability, it also introduces significant security challenges, including unauthorized access, data breaches, and cyberattacks. Secure medical image cryptanalysis has therefore become a critical research area, focusing on evaluating and strengthening encryption techniques used in healthcare systems. Recent advancements in artificial intelligence, particularly deep learning and quantum neural networks (QNNs), have opened new avenues for enhancing medical image security. Quantum neural networks leverage principles of quantum computing such as superposition and entanglement to improve computational efficiency and encryption strength. Additionally, deep learning-based cryptographic techniques enable intelligent key generation, anomaly detection, and attack mitigation. This paper presents a comprehensive review of secure medical image cryptanalysis techniques using quantum neural networks in IoT-enabled cloud storage environments. The study explores hybrid approaches combining quantum-inspired algorithms, deep learning models, and cryptographic techniques to improve data confidentiality, integrity, and availability. Recent research highlights the effectiveness of quantum-enhanced encryption frameworks, such as quantum key distribution and hybrid cryptographic systems, in protecting sensitive medical data. Despite these advancements, challenges such as high computational complexity, limited quantum hardware availability, and scalability issues persist. This review analyses recent developments, identifies research gaps, and provides future directions for developing secure and efficient medical image cryptanalysis systems.
Abstract The integration of quantum-enhanced blockchain and digital twin technologies offers a potent solution for advancing healthcare data security, particularly in ensuring the integrity and confidentiality of medical records. This paper presents a comprehensive framework that leverages quantum computing principles to enhance the security of blockchain networks, while digital twins are utilized for securely managing and accessing medical records. The proposed Quantum-Enhanced Blockchain Architecture employs quantum key distribution protocols and lattice-based cryptography to protect against quantum attacks, ensuring a secure and scalable decentralized system. The Digital Twin Framework for Medical Record Confidentiality integrates encryption and access control mechanisms to safeguard patient data. A novel Healthcare Encryption Algorithm (HEA) is proposed for encrypting and decrypting data in the digital twin, ensuring confidentiality and privacy. Furthermore, the Quantum-Enhanced Blockchain and Digital Twin Integration Process (QBDTIP) outlines the steps involved in securely managing medical records using these technologies. Experimental results demonstrate the system's robustness against cyber-attacks, scalability, interoperability, and effectiveness in ensuring data integrity and confidentiality. Overall, this research highlights the transformative potential of integrating quantum-enhanced blockchain and digital twin technologies for enhancing healthcare data security.