A Scalable Quantum-Intelligent Blockchain Framework for Secure Body Area Networks: Design of a Deep Quantum Neural Architecture for Privacy-Preserving Healthcare Communications
The development of the application of making use of Wireless Body Area Networks (WBANs) in medically created systems has presented fresh security concerns, in particular with the advent of quantum computing threatening the conventional cryptographic ideas. To proposes a new model Scalable Quantum-Intelligent Blockchain Framework (SQIBF) and a Deep Quantum Neural Architecture (DQNA) to privacy-preserving healthcare communications. The proposed framework is a synergistic approach of post-quantum lattice-based cryptography, in combination with quantum key protocols distribution schemes with federated quantum machine learning to develop multi-layered security infrastructure of the body sensor networks. We introduce Quantum Enhanced Consensus Mechanism (QECM), which deploys quantum random number generation to deliver Byzantine fault tolerance, and which can withstand symptoms of quantum-enabled parties by 97.5. The DQNA implements variational quantum gate scale used to detect textbook anomalies and categorize confidential medical data variational quantum circuit with a parameterized scale according to parameters to support the 94.3 percent mark on benchmark medical datasets with a differential privacy clarification. Experimental Assessment Simulations on simulated quantum systems and experimental WBAN testbeds have shown that it, SQIBF, can decrease the key establishing latency by approximately 40 percent when compared to classical blockchain methods using resource optimizing biosensors resource exploratory consumptions. Its model ensures the compliance of the HIPAA and GDPR requirements and homomorphic encryption layers with zero-knowledge proofs. Scalability of performance benchmarking implies up to 10,000 active sensor nodes and sub-millisecond finality of transaction. The present work introduces an end-to-end quantum resistant system to facilitate the muchneeded transitional process of quantum computing, distributed ledger technology, and health care IoT and establishes a platform of safe telemedicine infrastructure in the next generation.
The development of the application of making use of Wireless Body Area Networks (WBANs) in medically created systems has presented fresh security concerns, in particular with the advent of quantum computing threatening the conventional cryptographic ideas. To proposes a new model Scalable Quantum-Intelligent Blockchain Framework (SQIBF) and a Deep Quantum Neural Architecture (DQNA) to privacy-preserving healthcare communications. The proposed framework is a synergistic approach of post-quantum lattice-based cryptography, in combination with quantum key protocols distribution schemes with federated quantum machine learning to develop multi-layered security infrastructure of the body sensor networks. We introduce Quantum Enhanced Consensus Mechanism (QECM), which deploys quantum random number generation to deliver Byzantine fault tolerance, and which can withstand symptoms of quantum-enabled parties by 97.5. The DQNA implements variational quantum gate scale used to detect textbook anomalies and categorize confidential medical data variational quantum circuit with a parameterized scale according to parameters to support the 94.3 percent mark on benchmark medical datasets with a differential privacy clarification. Experimental Assessment Simulations on simulated quantum systems and experimental WBAN testbeds have shown that it, SQIBF, can decrease the key establishing latency by approximately 40 percent when compared to classical blockchain methods using resource optimizing biosensors resource exploratory consumptions. Its model ensures the compliance of the HIPAA and GDPR requirements and homomorphic encryption layers with zero-knowledge proofs. Scalability of performance benchmarking implies up to 10,000 active sensor nodes and sub-millisecond finality of transaction. The present work introduces an end-to-end quantum resistant system to facilitate the muchneeded transitional process of quantum computing, distributed ledger technology, and health care IoT and establishes a platform of safe telemedicine infrastructure in the next generation.
The emergence of 5G networks has revolutionized healthcare delivery by enabling communication with ultra-low latency, massive connectivity, and quick access to IoT devices' medical data. The digitalization of healthcare has, however, introduced privacy, data security, and scalability as major challenges, and the issues have been worsened by the rapid digitalization of the healthcare industry. The current review paper is a comprehensive evaluation of the role of Quantum Machine Learning (QML) as a paradigm for data handling with privacy preserved in the healthcare ecosystems supported by 5G. It collects advancements in quantum cryptography, Quantum Key Distribution (QKD), federated learning, and blockchain technology to explore how the combining of such technologies will enhance the confidentiality, interoperability, and real-time decision-making of the data. The review arranges and compares the state-of-theart research of 2019-2025, in which the trends, architectures, and security protocols that link classical and quantum methods in healthcare analytics become visible. It also addresses the main hurdles like quantum hardware limitations, interoperability gaps, and the tuning of federated models, and it indicates potential future research paths for the development of healthcare infrastructure that is scalable, reliable, and quantum-resistant. In conclusion, this paper gives a thorough understanding of how quantum intelligence can alter the data security and privacy landscape in 5G-enabled smart healthcare systems.
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.
Because many modern healthcare security systems rely on traditional cryptographic primitives that are susceptible to quantum attacks, the quick development of quantum computing poses previously unheard-of risks. With the increasing use of wearable sensors, telemedicine, AI-driven diagnostics and distributed clinical databases, as well as the increasing digitisation of healthcare, the need for quantum-resilient protection becomes critical. The integration of quantum and post-quantum cryptography (PQC) as fundamental security measures for upcoming intelligent healthcare ecosystems is thoroughly examined in this chapter. We summarise recent developments in cryptographic standards, secure communication protocols, AI-enabled defence mechanisms and blockchain-based data integrity systems using a PRISMA-guided systematic review. With an emphasis on performance, scalability and resistance to Shor- and Grover-based attacks, we further develop a simulation framework to assess hybrid quantum, classical security architectures under adversarial quantum scenarios. Our results show that while hash-based signatures provide ultra-long-term integrity for genomic and archival medical data, lattice-based PQC schemes, in particular CRYSTALS-Kyber and Dilithium, show strong suitability for electronic health record (EHR) encryption, secure IoMT communication and federated learning aggregation. A unified quantum-resilient architecture incorporating PQC, blockchain consensus, AI-based anomaly detection and quantum key distribution (QKD) is one of the theoretical contributions. In summary, we highlight the potential of quantum homomorphic AI, neuromorphic cryptosystems, quantum biometrics and adaptive threat intelligence to develop a safe, reliable and scalable paradigm for quantum-ready healthcare.
The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
Touch Healthcare (ZTH) in the 6G era envisions fully autonomous, intelligent, and secure healthcare systems enabled by IoT, quantum communication, and decentralized cloud infrastructures. As IoT devices proliferate in smart healthcare, they generate vast volumes of sensitive physiological data, requiring robust, scalable, and future-proof protection mechanisms. This paper presents a Quantum-Secured Hybrid Encryption Framework empowered by blockchain and Zero-Touch principles to ensure end-to-end confidentiality, integrity, and trust. The framework integrates a trusted authority for access control, quantum key controllers for cryptographic key generation, and secure data vaults for encrypted storage. Blockchain-enabled consensus nodes function as proxy service nodes, eliminating reliance on centralized service providers while securely managing cipher-text transactions in a distributed manner. The proposed system resists replay, side-channel, Sybil, and eavesdropping attacks and minimizes tampering risks through targeted quantum-based verification. Simulated results demonstrate a 93.33% accuracy rate, with discrepancies primarily caused by quantum device noise. By combining quantum encryption and blockchain within an AI-driven Zero-Touch architecture, this framework establishes a secure, decentralized, and resilient model for next-generation healthcare data sharing over 6G networks. Zero-Touch Healthcare (ZTH) in the 6G era envisions fully autonomous, intelligent, and secure healthcare systems enabled by IoT, quantum communication, and decentralized cloud infrastructures. As IoT devices proliferate in smart healthcare, they generate vast volumes of sensitive physiological data, requiring robust, scalable, and future-proof protection mechanisms. This paper presents a Quantum-Secured Hybrid Encryption Framework empowered by blockchain and Zero-Touch principles to ensure end-to-end confidentiality, integrity, and trust. The framework integrates a trusted authority for access control, quantum key controllers for cryptographic key generation, and secure data vaults for encrypted storage. Blockchain-enabled consensus nodes function as proxy service nodes, eliminating reliance on centralized service providers while securely managing cipher-text transactions in a distributed manner. The proposed system resists replay, side-channel, Sybil, and eavesdropping attacks and minimizes tampering risks through targeted quantum-based verification. Simulated results demonstrate a 93.33% accuracy rate, with discrepancies primarily caused by quantum device noise. By combining quantum encryption and blockchain within an AI-driven Zero-Touch architecture, this framework establishes a secure, decentralized, and resilient model for next-generation healthcare data sharing over 6G networks.