在基于IoMT的系统中实现有效的健康数据识别和分类.
Afnan Alsadhan1, Areej Alhogail1, Hessah A Alsalamah1
1Information Systems Department, College of Computer and Information Science, King Saud University, Riyadh P.O. Box 145111, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
医疗物联网 (IoMT) 框架SDAIPA使用HIPAA和SDAIA原则对健康数据风险进行分类. 这确保了对敏感的IoMT数据的更好的隐私保护和监管合规性.
科学领域:
- 医疗信息学 医疗信息学
- 网络安全 网络安全
- 健康数据管理 管理健康数据
背景情况:
- 医疗物联网 (IoMT) 能够实现个性化的医疗保健,但由于敏感数据交换,会带来重大隐私风险.
- 有效的数据识别和分类 (DIC) 对于保护IoMT数据,确保监管合规性和优化数据管理至关重要.
研究的目的:
- 引入SDAIPA (SDAIA-HIPAA),这是一个标准化的混合框架,用于对IoMT健康数据进行分类.
- 将HIPAA和SDAIA原则与双重风险评估 (独特性和潜在危害) 整合起来,以系统地对IoMT数据进行分类.
主要方法:
- 开发了一个混合分类框架 (SDAIPA),集成HIPAA和SDAIA原则.
- 采用双重风险视角,考虑数据的独特性和潜在的危害.
- 通过专家领域审查验证了框架.
主要成果:
- SDAIPA提供了一个结构化,与法规一致的过程,用于对IoMT健康数据的敏感性进行分类.
- 该框架有助于有效地分配加密资源,优先考虑高风险数据,如基因组或位置信息.
- 专家验证证实了该框架对于敏感性意识的IoMT数据管理的实用性.
结论:
- SDAIPA为管理IoMT数据提供了一个实用的参考,重点是隐私和监管调整.
- 该框架支持医疗保健提供者,政策制定者和人工智能开发者在智能医疗保健系统中加强数据保护.
- 在不断发展的IoMT生态系统中,SDAIPA促进了对敏感健康数据的比例和监管一致的保护.
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