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Location-dependent disclosure risk based decision support framework for persistent authentication in pervasive computing applications

机译:普适计算应用中用于持久身份验证的基于位置的公开风险的决策支持框架

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摘要

In pervasive computing applications (e.g. electronic health records), the amount of information permissible to be shared or accessed by mobile users results in high disclosure risks. Obfuscation techniques are desirable in reducing the impact of disclosing confidential information but with a significant loss of utility of information content. Thus, accesses to confidential data by mobile users need to be controlled so as to minimize the disclosure risks. To achieve these requirements, we propose a novel location-dependent disclosure risk based decision support framework for persistent authentication and data access management. We have derived the location dependent identity based disclosure risks at record level and file level by using the search theory and entropy. We have experimentally evaluated our proposed model using multi-level security model and fuzzy sets. We have further proved that our proposed technique can significantly reduce the impact of common privacy attacks by performing a comprehensive security analysis. In conclusion, this research presents a novel location-dependent disclosure risk-based decision support framework persistent authentication and a pragmatic data access management approach for highly privacy-sensitive pervasive computing applications. (C) 2015 Elsevier B.V. All rights reserved.
机译:在普及的计算应用程序(例如电子健康记录)中,移动用户允许共享或访问的信息量会导致很高的公开风险。希望使用混淆技术来减少泄露机密信息的影响,但会大大损失信息内容的实用性。因此,需要控制移动用户对机密数据的访问,以最小化公开风险。为了实现这些要求,我们提出了一种基于位置依赖于公开风险的新颖决策支持框架,用于持久身份验证和数据访问管理。通过使用搜索理论和熵,我们得出了记录级别和文件级别基于位置的基于身份的公开风险。我们已经使用多级安全模型和模糊集实验性地评估了我们提出的模型。我们进一步证明了我们提出的技术可以通过执行全面的安全分析来显着减少常见隐私攻击的影响。总而言之,这项研究提出了一种针对高度隐私敏感的普适计算应用的新颖的基于位置的公开风险为基础的决策支持框架,以及一种实用的数据访问管理方法。 (C)2015 Elsevier B.V.保留所有权利。

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