Nowrozy Raza, Ahmed Khandakar, Wang Hua
Victoria University, Melbourne, VIC, Australia.
PLoS One. 2025 Jan 6;20(1):e0310553. doi: 10.1371/journal.pone.0310553. eCollection 2025.
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.
随着数字医疗的发展，电子健康记录（EHR）的安全性变得越来越重要。本研究提出了GPT-Onto-CAABAC框架，该框架集成了生成式预训练变换器（GPT）、医学法律本体和上下文感知属性基访问控制（CAABAC），以增强电子健康记录访问安全性。与传统模型不同，GPT-Onto-CAABAC能够动态解释策略并适应不断变化的医疗和法律环境，提供定制化的访问控制解决方案。通过实证评估，该框架被证明能通过使访问决策与复杂的法规和情境要求精确匹配，有效提高电子健康记录的安全性。研究结果表明，它在访问控制必须符合严格合规性和适应性标准的领域具有更广泛的适用性。
