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HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications

A novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology, which offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.
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Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models that ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers.
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A New Blockchain-Based Authentication Framework for Secure IoT Networks

This work designs a novel IoT approach based on a trust-aware security approach that increases security and privacy while connecting outstanding IoT services and is compared with other benchmark frameworks based on extensive simulation results.
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Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography

This research developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search to result in a more efficient blockchain-based IoT system as compared to benchmark models.
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A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

A blockchain-based deep-learning framework is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work to achieve the target of security and privacy.
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Adaptive and Context-Aware Authentication Framework Using Edge AI and Blockchain in Future Vehicular Networks

    Aitizaz Ali
    Computer Science, Engineering
    STAP Journal of Security Risk Management
  • 2024
An adaptive and context-aware authentication framework that integrates Edge Artificial Intelligence (AI) with blockchain technology to secure vehicular communication and highlights the potential of hybrid AI-blockchain models to enhance the security, scalability, and accountability of future vehicular networks.
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An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

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A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS

A lightweight authentication and data preservation scheme for IoT based-CPS utilizing deep learning (DL) to facilitate decentralized authentication among legal devices is proposed and reveals incredible advancement in terms of comparative parameters in comparison with benchmark models.
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Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

The potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis is explored, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine.
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Streamlining supply chains: An efficiency-driven permissioned blockchain framework for data reduction

This paper seeks into the technical foundations of permissioned blockchains, highlighting their suitability for supply applications where confidentiality and controlled access are imperative, and examines various data reduction methodologies, emphasizing their role in minimizing redundant data, optimizing communication, and enabling real-time decision-making.
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