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Profile Matching for IoMT: A Verifiable Private Set Intersection Scheme.

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    This study introduces a verifiable private set intersection scheme for fine-grained profile matching in the Internet of Medical Things (IoMT). It enhances patient privacy and ensures secure data sharing against untrusted cloud servers.

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    Area of Science:

    • Medical Informatics
    • Cybersecurity
    • Data Privacy

    Background:

    • The Internet of Medical Things (IoMT) leverages advancements in IoT, 5G, and AI for healthcare.
    • Profile matching in IoMT enables medical information sharing based on symptom attributes.
    • Sharing sensitive patient data for matching poses significant privacy risks.

    Purpose of the Study:

    • To propose a verifiable private set intersection scheme for fine-grained profile matching in IoMT.
    • To address privacy concerns associated with sharing sensitive patient health information.
    • To ensure secure and efficient medical data sharing.

    Main Methods:

    • Developed a verifiable private set intersection scheme.
    • Implemented multi-tag division for fine-grained privacy data operations.
    • Utilized re-encryption techniques for enhanced patient privacy protection.
    • Incorporated a verifiable mechanism to ensure cloud server computation correctness.

    Main Results:

    • The proposed scheme enables fine-grained profile matching while protecting patient privacy.
    • Security analysis confirms resistance against untrusted cloud servers.
    • Performance simulations show improved efficiency by reducing bilinear pair usage.

    Conclusions:

    • The scheme effectively balances secure medical data sharing with robust patient privacy in IoMT.
    • It offers a practical solution for privacy-preserving profile matching in healthcare applications.
    • The verifiable mechanism enhances trust in cloud-based IoMT data processing.