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首页> 外文期刊>Journal of ambient intelligence and smart environments >SoTRAACE for smart security in ambient assisted living
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SoTRAACE for smart security in ambient assisted living

机译:SoTRAACE在环境辅助生活中提供智能安全

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摘要

Ambient Assisted Living (AAL) solutions have been conquering an important place among strategies to promote ageing in place and address the societal challenges of population ageing. Related available smart solutions and their pervasiveness raise security challenges requiring more flexible and better adapted characteristics. However, such solutions are frequently described in the literature without any reference to access control and the few existing works in the AAL field focus mostly on authentication or physical access control. This paper describes the SoTRAACE (Socio-Technical Risk-Adaptable Access Control) model, designed to better adapt users' access control needs to each AAL security context. SoTRAACE takes into account contextual, technological and user's interaction profiling functionalities and performs a quantitative and qualitative risk assessment analysis to support a smart decision-making on the most secure, private and usable way to access and display information. SoTRAACE's unique advantages for improved availability and privacy are discussed in contrast with existing access control models and within two AAL for mental health use case scenarios. SoTRAACE's versatility adapts to different situations and user's goals, whether these are patient or caregiver oriented, constituting an innovative and complete proposal for enhanced trust and security in AAL or similar smart environments.
机译:环境辅助生活(AAL)解决方案一直在促进就地老龄化和应对人口老龄化的社会挑战的战略中占据重要位置。相关的可用智能解决方案及其普遍性带来了安全挑战,需要更灵活,更适应的特性。但是,在文献中经常描述这样的解决方案,而没有任何访问控制的参考,并且AAL领域中的一些现有工作主要集中在认证或物理访问控制上。本文介绍了SoTRAACE(适应社会技术风险的访问控制)模型,该模型旨在使用户的访问控制需求更好地适应每个AAL安全上下文。 SoTRAACE考虑了上下文,技术和用户的交互概要分析功能,并执行了定量和定性的风险评估分析,以支持以最安全,私有和可用的方式访问和显示信息的明智决策。与现有的访问控制模型相比,SoTRAACE在改善可用性和隐私性方面的独特优势得到了讨论,并在两个AAL中针对心理健康用例场景进行了讨论。 SoTRAACE的多功能性可适应不同的情况和用户的目标,无论这些目标是针对患者还是护理人员,构成了一种新颖而完整的建议,旨在增强AAL或类似智能环境中的信任和安全性。

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