PriMe: Human-centric privacy measurement based on user preferences towards data sharing in mobile participatory sensing systems

Rui Liu, Jiannong Cao, S. VanSyckel, Wenyu Gao
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引用次数: 6

Abstract

Mobile participatory sensing systems allow people with mobile devices to collect, interpret, and share data from their respective environments. One of the main obstacles for long-term participation in such systems is the users' privacy concerns. Due to the nature of these systems, users have to agree to provide some personalized information. Typically, however, people are reluctant to share any information, as it may be sensitive. This is especially the case if the content of the data in question is not completely transparent. In order to increase users' willingness to participate in such systems, we should help users identify which data they can share without violating their personal privacy policies. However, the perception of how sensitive a piece of information is may differ from user to user. In this paper, we propose the human-centric privacy measurement method PriMe, which quantifies privacy risks based on user preferences towards data sharing in participatory sensing systems. Further, we implemented and deployed PriMe in the real world as a user study for evaluation. The study shows that PriMe provides accurate ratings that fit users' individual perceptions of privacy, and is accepted by users as a trustworthy tool.
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PriMe:基于用户对移动参与式传感系统数据共享偏好的以人为中心的隐私测量
移动参与式传感系统允许拥有移动设备的人们从各自的环境中收集、解释和共享数据。长期参与此类系统的主要障碍之一是用户的隐私问题。由于这些系统的性质,用户必须同意提供一些个性化信息。然而,通常情况下,人们不愿意分享任何信息,因为它可能是敏感的。如果所讨论的数据内容不是完全透明的,情况尤其如此。为了增加用户参与此类系统的意愿,我们应该帮助用户识别哪些数据可以在不违反其个人隐私政策的情况下共享。然而,对于一条信息的敏感程度的感知可能因用户而异。本文提出了以人为中心的隐私测量方法PriMe,该方法基于参与式感知系统中用户对数据共享的偏好来量化隐私风险。此外,我们在现实世界中实现并部署了PriMe,作为评估的用户研究。研究表明,PriMe提供了准确的评分,符合用户个人对隐私的看法,被用户接受为值得信赖的工具。
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