GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute
<|reference_start|>GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute: 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.<|reference_end|>
@article{nowrozy2024gpt,,
    title={GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control
  Model Anchored by Compliance, Context and Attribute},
    author={Raza Nowrozy, Khandakar Ahmed, Hua Wang},
    journal={arXiv preprint arXiv:2403.08264},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08264},
    primaryClass={cs.CY cs.AI cs.CR}
}