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GPT, ontology, and CAABAC: A tripartite personalized access control model anchored by compliance, context and attribute

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  • Raza Nowrozy
  • Khandakar Ahmed
  • Hua Wang

Abstract

As digital healthcare evolves, the security of electronic health records (EHR) becomes increasingly crucial. This study presents the GPT-Onto-CAABAC framework, integrating Generative Pretrained Transformer (GPT), medical-legal ontologies and Context-Aware Attribute-Based Access Control (CAABAC) to enhance EHR access security. Unlike traditional models, GPT-Onto-CAABAC dynamically interprets policies and adapts to changing healthcare and legal environments, offering customized access control solutions. Through empirical evaluation, this framework is shown to be effective in improving EHR security by accurately aligning access decisions with complex regulatory and situational requirements. The findings suggest its broader applicability in sectors where access control must meet stringent compliance and adaptability standards.

Suggested Citation

  • Raza Nowrozy & Khandakar Ahmed & Hua Wang, 2025. "GPT, ontology, and CAABAC: A tripartite personalized access control model anchored by compliance, context and attribute," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-45, January.
    • Handle: RePEc:plo:pone00:0310553
    DOI: 10.1371/journal.pone.0310553
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    References listed on IDEAS

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    2. Pekka Ruotsalainen & Bernd Blobel, 2020. "Health Information Systems in the Digital Health Ecosystem—Problems and Solutions for Ethics, Trust and Privacy," IJERPH, MDPI, vol. 17(9), pages 1-15, April.
    3. Faheem Ahmad Reegu & Hafiza Abas & Yonis Gulzar & Qin Xin & Ali A. Alwan & Abdoh Jabbari & Rahul Ganpatrao Sonkamble & Rudzidatul Akmam Dziyauddin, 2023. "Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
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