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A Fast and Efficient Entity Resolution Approach for Preserving Privacy in Mobile Data

机译:快速有效的实体解析方法,可保护移动数据中的隐私

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With the advent of mobile networking and the widespread adoption of smartphone devices, a number of location-based services have emerged, where users actively participate by sharing and receiving mobility data. However, the collection and analysis of user mobility data, such as user location information and trajectory data, especially when exploited together with external sources, such as social networks that often provide rich and publicly available information, can reveal sensitive user information. This paper proposes an approach based on entity resolution which enables users to disclose their mobility information without compromising their privacy, even if these data are linked with external publicly available information. We present detailed experimental results using four real datasets to illustrate that our approach is practical, efficient and effectively preserves privacy by eliminating potential links among the data.
机译:随着移动网络的出现和智能手机设备的广泛采用,出现了许多基于位置的服务,用户通过共享和接收移动性数据积极参与其中。但是,用户移动性数据(例如用户位置信息和轨迹数据)的收集和分析,尤其是与外部资源(例如经常提供丰富且公开可用信息的社交网络)一起使用时,可能会泄露敏感的用户信息。本文提出了一种基于实体解析的方法,即使这些数据与外部公共可用信息相关联,用户也可以在不损害其隐私的情况下披露其移动性信息。我们使用四个真实的数据集展示了详细的实验结果,以说明我们的方法是实用,高效的,并且通过消除数据之间的潜在联系来有效地保护隐私。

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