用协作模型保护普适环境中的用户隐私

F. Rahman, Md. Endadul Hoque, Sheikh Iqbal Ahamed, M. A. Alam
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引用次数: 3

摘要

在普遍环境中,隐私是对上下文感知最常被引用的批评。上下文感知的普及应用程序存在捕获大量用户活动的漏洞。无论这种数据捕获是否是一种实际威胁,用户对这种可能性的感知可能会阻止他们使用许多有用的普及应用程序。到目前为止,在上下文感知的普及应用程序中,位置数据一直是使用户匿名的主要焦点。然而,在现实中,用户匿名取决于特定应用程序收集的所有隐私敏感数据。保护用户隐私,或者换句话说,在匿名器的帮助下保护用户匿名,容易出现单点故障。在本文中,我们提出了一种形式协作模型(FCM),该模型在没有匿名器的情况下保持了用户的匿名性。该模型还可以量化在向不可信的服务提供商请求服务时所涉及的隐私数量。由于我们的模型可以在请求将要被放置时量化服务请求者获得的隐私,因此它允许用户在普遍的环境中了解他们的总体隐私偏好情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preserving User Privacy in Pervasive Environments with a Collaborative Model
Privacy is the most often cited criticism of context awareness in pervasive environments. Context aware pervasive applications have the vulnerabilities of capturing extensive portions of users' activities. Whether such data capture is an actual threat or not, users' perceptions of such possibilities may discourage them from using many useful pervasive applications. So far, in context aware pervasive applications, location data has been the main focus to make users anonymous. However in reality, user anonymity depends on all the privacy sensitive data collected by a particular application. Preserving user privacy or in other words, protecting user anonymity with the help of an anonymizer has the susceptibility of a single point of failure. In this paper, we propose a Formal Collaborative Model (FCM) that preserves users' anonymity without an anonymizer. This model can also quantify the amount of privacy at stake at the time of asking for services from untrustworthy service providers. Since our model can quantify service requester's achieved privacy when a request is going to be placed, it allows the users to be aware of their overall privacy preference situation in a pervasive environment.
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