ABSTRACT Objective To design a secure Federated Learning (FL) framework for Internet of Medical Things (IoMT) that protects sensitive patient data from both classical and quantum attacks. Methods Proposed the QSFedMA‐IoMT protocol integrating quantum and classical security techniques. Utilized entanglement‐based E91 protocol for generating a highly secure root key to establish trust. Applied BB84 protocol for efficient generation of per‐round session keys during FL updates. Incorporated classical cryptographic scheme AES‐GCM for secure communication. Employed privacy‐enhancing techniques such as norm‐clipping and Gaussian noise to mitigate information leakage during model training. Results Our work demonstrates robust resistance against both classical and quantum adversaries, while enhancing data privacy through secure key distribution and differential privacy mechanisms. It ensures the integrity of model updates within the federated learning process and achieves an effective balance between strong security guarantees and computational efficiency, making it well‐suited for IoMT environments. Conclusion The QSFedMA‐IoMT protocol delivers a robust and practical hybrid framework for securing federated learning in healthcare systems. By integrating E91 and BB84 protocols, it strengthens key management and trust establishment. The combination of quantum security with classical privacy‐preserving techniques ensures resilience, scalability, and efficiency. Overall, this work provides a promising direction for secure and privacy‐aware federated learning in next‐generation IoMT applications.

ABSTRACT Blockchain, an immutable, decentralized, and distributed ledger technology, has the potential to revolutionize the sharing and access of confidential data, particularly in the healthcare sector, where patient‐centric systems benefit greatly from its robust security features and ability to ensure data integrity. Recognizing the potential, this paper reviews current research on blockchain‐based healthcare architectures that focus on patient‐centric solutions, exploring the present state of these architectures and highlighting the growing interest in blockchain applications for enhancing healthcare. The review identifies key trends, challenges, and opportunities within this domain, analyzing the evolution of research over recent years, the types of studies conducted, their geographical distribution, and publication channels. It delves into the prevalent use cases, primary challenges addressed, and contributions of various studies, covering common blockchain platforms, types of blockchains, and the implementation of smart contracts. Guided by four critical questions aimed at improving security and privacy, streamlining patient consent management, fostering interoperability, and ensuring compliance with healthcare data regulations, this research underscores the importance of patient data security through advanced encryption techniques, access controls, and decentralized storage solutions. It also highlights the benefits of patient‐controlled data sharing, empowering individuals with greater control over their health information. Despite progress, the review identifies areas requiring further exploration, such as advanced security threats, nuanced and dynamic consent models, comprehensive interoperability solutions, and alignment with evolving healthcare regulations. Addressing these gaps necessitates research into scalable privacy‐preserving blockchain architectures that prioritize dynamic consent management and interoperability standards, thus making regulatory compliance possible in an ever‐changing healthcare environment.

ABSTRACT Internet of Drones (IoD) is one of the most beneficial and has many versatile applications like Surveillance and Security, Delivery and Logistics, Environmental Monitoring, Agriculture, and so forth. The IoD network is crucial for collecting sensitive data like geo‐coordinates, vehicle traffic data, and property details while surveying the various deployment locations in smart cities. The communication between users and drones can be compromised over insecure wireless channels by multiple attacks such as Man‐in‐the‐middle‐attack, Denial of Service, and so forth. Many schemes have already been propounded in the field of IoD. Still, many of them cannot address the resource constraints problem of drones, and existing protocols have higher computation and communication costs. Therefore, this paper has proposed a robust and efficient Hyper‐Elliptic Curve‐based authentication scheme (REHAS), which provides a session key for secure communication. Artificial Identities are generated using a hash function and random numbers. Fuzzy Extractor is used for user biometric authentication, which makes the smart device secure when lost. HECC is used with a smaller bit size of 80 bits rather than ECC of 160 bits. The security of the REHAS has been ensured using Scyther simulation. Furthermore, the resilience, safety, and robustness of REHAS are ensured by Informal security analysis. Lastly, a comparative study of the REHAS has been performed with other related Authentication and key agreement (AKA) protocols regarding communication cost, Computation cost, and security features, demonstrating that REHAS incurred less computation cost (6.7171 ms), communication overhead (1696 bits), and energy consumption (22.5 mJ) than other existing AKA schemes.