GPT,本体学和CAABAC:一个三方个性化访问控制模型,以合规性,上下文和属性为基础
Raza Nowrozy1, Khandakar Ahmed1, Hua Wang1
1Victoria University, Melbourne, VIC, Australia.
PloS one
|January 6, 2025
概括
本研究介绍了GPT-Onto-CAABAC,这是一个新的框架,可以增强电子健康记录 (EHR) 的安全性. 它动态调整访问控制以适应不断变化的医疗保健法规和法律环境,以改善数据保护.
科学领域:
- 数字健康安全数字健康安全
- 医疗信息学 医疗信息学
- 医疗保健中的网络安全
背景情况:
- 医疗保健日益数字化,需要对电子健康记录 (EHR) 提供强大的安全性.
- 传统的访问控制模型难以适应医疗保健中的动态法律和情境要求.
研究的目的:
- 介绍GPT-Onto-CAABAC框架,以提高EHR访问安全性.
- 证明框架能够动态解释政策并适应不断变化的环境的能力.
主要方法:
- 将生成预训练变压器 (GPT) 与医疗法律本体学的整合.
- 实现基于上下文的属性访问控制 (CAABAC) 以实现动态的政策解释.
- 经验评估框架在调整访问决策与监管和情境需求方面的有效性.
主要成果:
- 该GPT-Onto-CAABAC框架有效地提高了EHR的安全性.
- 该框架证明了访问决策与复杂的监管和情境要求的准确协调.
- 经验评估证实了框架的安全性能提高.
结论:
- 在不断变化的数字环境中,GPT-Onto-CAABAC为保护敏感健康数据提供了一个有希望的解决方案.
- 该框架的适应性和合规驱动的方法表明,在受监管的行业更广泛的适用性.
- 这项研究有助于在数字医疗保健中推进安全和合规的访问控制.
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