Secure remote sharing of medical images is essential for advancing telemedicine, accurate diagnostics, and collaborative healthcare delivery. The traditional cryptography techniques are fast turning to be susceptible to quantum computing risks and generally are less efficient in high throughput medical settings. To counter such a shortcoming, we present an innovative quantum-oriented solution that utilizes optimized methods of secure and real-time medical image transmission over a long-distance network. In this method, Quantum Key Distribution (BB84 protocol), hybrid cryptography, quantum informed optimization, and quantum steganography are used to provide efficient high-fidelity and low latency transmission of images. The QKD is used to securely create symmetric keys that are to be used in the AES 256 encryption of the image and Elliptic Curve Cryptography is applied so that the metadata can be kept confidential. Immutable and transparent access logs are kept by using the blockchain technology. In order to even improve the performance, a dynamically tuning encryption parameters using a Quantum Genetic Algorithm can be applied to eliminate the computing overhead. Experimental testing with NIH Chest X-ray dataset shows an average encryption and decryption speed of 38.1 milliseconds, with a success rate of more than 25% relative to the conventional post-quantum cryptographic system. The results of a secure transmission show a decrease in latency of 12.5% over the TLS-enhanced QKD. The system keeps the Quantum Bit Error Rate at less than 5% under realistic noise levels and retains more than 75% of the key material obtained. In quantum steganography, the level of accuracy to detect tampering is 98.6%, hence data integrity. The solution has shown the capability to integrate quantum-level security, computing performance, and regulatory durability to become a trusted and long-term process of safe sharing of medical images in modern healthcare systems.
Secure remote sharing of medical images is essential for advancing telemedicine, accurate diagnostics, and collaborative healthcare delivery. The traditional cryptography techniques are fast turning to be susceptible to quantum computing risks and generally are less efficient in high throughput medical settings. To counter such a shortcoming, we present an innovative quantum-oriented solution that utilizes optimized methods of secure and real-time medical image transmission over a long-distance network. In this method, Quantum Key Distribution (BB84 protocol), hybrid cryptography, quantum informed optimization, and quantum steganography are used to provide efficient high-fidelity and low latency transmission of images. The QKD is used to securely create symmetric keys that are to be used in the AES 256 encryption of the image and Elliptic Curve Cryptography is applied so that the metadata can be kept confidential. Immutable and transparent access logs are kept by using the blockchain technology. In order to even improve the performance, a dynamically tuning encryption parameters using a Quantum Genetic Algorithm can be applied to eliminate the computing overhead. Experimental testing with NIH Chest X-ray dataset shows an average encryption and decryption speed of 38.1 milliseconds, with a success rate of more than 25% relative to the conventional post-quantum cryptographic system. The results of a secure transmission show a decrease in latency of 12.5% over the TLS-enhanced QKD. The system keeps the Quantum Bit Error Rate at less than 5% under realistic noise levels and retains more than 75% of the key material obtained. In quantum steganography, the level of accuracy to detect tampering is 98.6%, hence data integrity. The solution has shown the capability to integrate quantum-level security, computing performance, and regulatory durability to become a trusted and long-term process of safe sharing of medical images in modern healthcare systems.
Healthcare systems are increasingly data-driven through EHRs, telemedicine, and IoT-based medical devices. However, cyber threats, poor interoperability, and data tampering expose the vulnerabilities of centralized systems. Blockchain offers immutability and transparency, yet its traditional cryptography faces risks from quantum computing, which can compromise existing encryption. To address this, the chapter proposes a quantum-enhanced blockchain framework integrating post-quantum cryptography (PQC), quantum key distribution (QKD), and hybrid quantum-classical consensus. The model strengthens confidentiality, integrity, and availability (CIA) of healthcare data while ensuring interoperability and regulatory compliance. Key applications include secure EHR and genomic data management, automated patient consent handling, transparent drug supply chain monitoring, and protection of clinical research. The chapter also discusses ethical, technical, and regulatory challenges, offering insights into building resilient, quantum-secure healthcare ecosystems.
As developments in quantum computing threaten to break existing encryption techniques safeguarding sensitive patient information in cloud-based medical systems, the healthcare sector is confronting never-before-seen cryptographic difficulties. Addressing unique needs like multi-decade data retention demands, legacy medical device limitations, and real-time patient care demands, the framework provides a specialized quantum-secure cryptographic design created particularly for healthcare settings. Three innovations are at the basis: a tiered implementation plan that dynamically allocates post-quantum protection level by medical data sensitivity categorisation, application to edge computing architecture that allows quantum-resistant security with resource-constrained medical devices without hardware enhancements, and healthcare interoperability-aware hybrid protocols to ensure continuous data transfer between FHIR, HL7, and DICOM standards in the event of cryptographic switchovers. The model uses NIST-standardized post-quantum algorithms such as CRYSTALS-Kyber and CRYSTALS-Dilithium (digital signatures) and SPHINCS+ (long-term integrity verification) tailored to healthcare with competing requirements of the highest security and lowest operations latency. The implementation strategies will include the application of phase-based implementation approaches that will include the infrastructure preparation phase through full integration of the ecosystem, with a special focus on the optimization of HIPAA compliance and alignment of regulatory frameworks. Performance optimization techniques leverage characteristic healthcare data access patterns, including patient-centric workflows, clinical department batch processing, and medical imaging parallelization strategies. The quantum-safe infrastructure establishes foundational capabilities for next-generation healthcare innovations, including precision medicine genomics platforms, federated artificial intelligence systems, and global health collaboration networks requiring cryptographic security guarantees spanning decades or centuries against both classical and quantum computational threats.
Background and Aims: Rising quantum hazards and flaws in conventional encryption make cloud-based healthcare data security harder. Quantum-Secure HealthChain, a new architecture using blockchain and quantum computing, improves medical data security, patient privacy, and data fidelity. Methods: To prevent quantum attacks, the proposed system uses Quantum Key Distribution (QKD) for safe cryptographic key exchange and quantum-resistant encryption. Blockchain technology secures medical records, while multi-layered encryption ensures data privacy. Quantum Biometric Authentication improves access control using quantum entanglement and biometric data. Key generation, encryption, blockchain storage, authentication, and decryption are system process steps. Experimental evaluation focuses on encryption speed, resource economy, throughput, and scalability using simulated healthcare data. Results: Experimental data demonstrate system strength and efficiency. Encryption and decryption perform consistently for 1 to 100 MB data sizes with negligible overhead. Throughput can reach 105 transactions per second under normal demand; CPU (82%) and memory (210 MB) utilization are low. Scalability studies show linear expansion lets the system handle increased data volumes and user demands without sacrificing performance. Security study confirms quantum attack, data corruption, and unauthorized access resistance. Conclusion: Quantum-Secure HealthChain offers a revolutionary method to cloud-based healthcare system security. Blockchain-quantum computing integration ensures strong authentication, safe key exchange, and quantum-resistant encryption. Its security, scalability, and efficiency make it a future-ready platform for safe medical data management, reducing quantum computing hazards.
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
In today's medical world, Medical Cyber-Physical Systems (MCPS) are becoming more and more important because they allow medical equipment that are linked to receive data, be monitored, and make decisions automatically in real time. However, strong and future-proof security measures are needed because medical data is sensitive and confidential; The Risk of getting exposed online is growing. Traditional cryptographic systems can be broken by attacks that use quantum mechanics and computers that are getting faster. This makes quantum-secured communication very important. By using Quantum Key Distribution (QKD), this study solves the important problem of keeping key management and sharing safe in MCPS. Our goal is to create a safe and effective quantum-enhanced encryption system that protects privacy, stability, and resistance to threats from other parties. The research suggests a mixed cryptography system that combines symmetric keys produced by quantum randomness with RSA-AES encryption methods. This would benefit from both the strengths of quantum randomness and well-known cryptographic standards. Using a seven-step QKD process to make a quantum key: random bit generation, photon state encoding, quantum transfer, basis reconciliation, spying detection, privacy amplification, and symmetric encryption via XOR are the steps that are used. The safe AES-GCM encryption of medical data is reinforced by RSA encryption of AES keys, providing two levels of privacy. Man-in-the-Middle (MITM), Brute-Force, Dictionary, Timing, and Chosen Cipher text Attacks (CCA) are some of the offender models used in the experimental review. Findings show that QKD provides strong protection against key theft and hacking, and the mixed RSA-AES system performs well in terms of encryption time, verification, and decoding accuracy. Attack modelling results show that the system is good at finding or reducing intrusions. The study creates a quantum-secure key sharing system that can be used on a large scale to protect important data in medical cyber-physical settings.
The rapid evolution of Internet of Things (IoT)-enabled healthcare systems has significantly increased the generation, transmission, and storage of medical images such as MRI, CT scans, and X-rays in cloud environments. While this advancement enhances remote diagnostics and telemedicine, it also introduces critical security challenges related to confidentiality, integrity, and privacy of sensitive patient data. Traditional cryptographic methods, including AES and RSA, are often inadequate in addressing the complex requirements of medical image security due to high computational overhead and vulnerability to emerging cyber threats. Consequently, recent research has shifted toward hybrid approaches integrating chaos theory, DNA encoding, deep learning, and quantum computing. This survey paper provides a comprehensive analysis of modern techniques and architectures for secure medical image cryptanalysis, with a particular focus on quantum neural networks (QNNs) in IoT-enabled cloud environments. The study reviews state-of-the-art encryption schemes such as hyperchaotic systems, quantum key distribution, and hybrid quantum-classical cryptography, highlighting their effectiveness in securing medical image transmission and storage. Additionally, the role of deep learning-based cryptographic models and quantum-inspired algorithms in improving robustness against attacks is examined. The paper further presents a comparative analysis of recent studies (2020–2023), identifying key performance metrics such as entropy, PSNR, computational efficiency, and resistance to statistical and differential attacks. Challenges including resource constraints of IoT devices, scalability issues, and limitations of current quantum hardware are also discussed. Finally, the paper outlines future research directions emphasizing the integration of quantum neural networks and lightweight cryptographic frameworks to ensure secure, efficient, and scalable healthcare systems.