The emergence of Internet of Medical Things (IoMT) for medical data collection and pre-processing has raised concerns regarding the privacy and security of the data. For many years, the decentralized technology of blockchain has ensured that data is secure and protected from intruders. However, with the emergence of quantum computing, blockchain is vulnerable because it relies on traditional cryptography. Therefore quantum-based blockchain emerged as a solution, it provides more security and ensures data integrity. In this study, MedQNet, a quantum-based platform integrating Quantum-based blockchain to store medical records, and additionally a Quantum-based cloud processing is proposed for secure medical data processing while preserving user privacy. A quantum-aided convolutional neural network (QACNN) is employed to analyze and extract patterns from EEG signals for normal and seizure brain activity detection, demonstrating superior stability and accuracy under quantum noise. Furthermore, a hierarchical quantum mechanics-based framework processes EEG signals for feature extraction, followed by an improved hybrid QACNN with arbitrary nonlinear kernels for classification, achieving exponential speedup over classical methods. The analytical assessment confirms that the quantum ledger framework effectively resists various quantum-based risks, including external intrusions, entanglement-based assaults, and interception-measure-replay attacks. By combining the security strengths of quantum blockchain with the computational power of quantum machine learning, this integrated approach ensures reliable, high-performance processing of EEG signals in IoMT, advancing privacy-preserving performance that reaches up to 96.78% accuracy for healthcare solutions.
The emergence of Internet of Medical Things (IoMT) for medical data collection and pre-processing has raised concerns regarding the privacy and security of the data. For many years, the decentralized technology of blockchain has ensured that data is secure and protected from intruders. However, with the emergence of quantum computing, blockchain is vulnerable because it relies on traditional cryptography. Therefore quantum-based blockchain emerged as a solution, it provides more security and ensures data integrity. In this study, MedQNet, a quantum-based platform integrating Quantum-based blockchain to store medical records, and additionally a Quantum-based cloud processing is proposed for secure medical data processing while preserving user privacy. A quantum-aided convolutional neural network (QACNN) is employed to analyze and extract patterns from EEG signals for normal and seizure brain activity detection, demonstrating superior stability and accuracy under quantum noise. Furthermore, a hierarchical quantum mechanics-based framework processes EEG signals for feature extraction, followed by an improved hybrid QACNN with arbitrary nonlinear kernels for classification, achieving exponential speedup over classical methods. The analytical assessment confirms that the quantum ledger framework effectively resists various quantum-based risks, including external intrusions, entanglement-based assaults, and interception-measure-replay attacks. By combining the security strengths of quantum blockchain with the computational power of quantum machine learning, this integrated approach ensures reliable, high-performance processing of EEG signals in IoMT, advancing privacy-preserving performance that reaches up to 96.78% accuracy for healthcare solutions.
Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). The blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. The blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This article innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis, and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy.
The convergence of blockchain technology and the Internet of Medical Things (IoMT) significantly enhances data security and interoperability in healthcare systems. However, emerging quantum computing capabilities pose critical threats to conventional cryptographic algorithms underpinning blockchain security. Moreover, as IoMT devices manage sensitive patient data and perform essential healthcare functions, ensuring robust protection against both classical and quantum attacks has become increasingly urgent. To mitigate these risks, this paper introduces a Post-Quantum Cryptography Integration Framework (PQCIF), designed to protect blockchain-enabled IoMT (BIoMT) systems and infrastructures against classical and quantum attacks. PQ-CIF is a hybrid framework leveraging NIST-standardized post-quantum cryptographic (PQC) algorithms combined with conventional public-private key pair cryptography schemes, strategically integrating them within BIoMT architectures for robust long-term security. We provide comprehensive evaluation metrics and present a detailed case study demonstrating the efficacy and minimal overhead of our proposed framework in a simulated healthcare blockchain network.
The rapid evolution of healthcare systems, coupled with advancements in the Internet of Medical Things (IoMT), has introduced unprecedented security and privacy challenges. Traditional security solutions fail to address emerging threats posed by quantum computing and AI-driven cyberattacks. This paper proposes an Integrative Quantum Blockchain and AI-Driven Security Framework for Healthcare Systems, incorporating Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), and AI-powered threat detection. The proposed framework enhances data integrity, authentication, and resistance against cyber threats, while optimizing energy efficiency. This paper presents the architecture, security benefits, performance analysis, and future research directions for integrating quantum blockchain and AI in healthcare security.
Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.
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.
The Internet of Medical Things (IoMT) is a compelling networking paradigm integrating wireless communications sensors, connected devices, and embedded computing technologies. The IoMT involves the collection of real-time health data using sophisticated medical sensors. In recent years, the IoMT has become increasingly significant within the broader context of the Internet of Things (IoT). It provides accessibility for health monitoring and poses security obstacles to safeguarding the confidentiality and privacy of patient data. Therefore, this article presents a blockchain-integrated quantum authentication scheme in sensor-assisted IoMT networks. The proposed concept utilizes blockchain technology to achieve efficient patient authentication without the need for third-party entities. In addition, a secure quantum authentication scheme is designed not to require patients to authenticate themselves when communicating with multiple doctors simultaneously. This protocol explicitly addresses how clinicians can misuse their professional roles toward patients in IoMT networks. An evaluation analysis assesses the proposed technique’s efficacy compared to existing authentication schemes. The performance analyses demonstrate that the proposed protocol is resilient against various security attacks. Also, the practical usability of the quantum authentication scheme proved its importance as a significant improvement in communication security for IoMT networks.