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

机译:一种快速高效的实体解决方法,用于保留移动数据中的隐私

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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,已经出现了许多基于位置的服务,用户通过共享和接收移动数据积极参与。然而,用户移动数据的收集和分析,例如用户位置信息和轨迹数据,特别是当与外部源一起被利用一起时,例如经常提供丰富和公开信息的社交网络,可以揭示敏感的用户信息。本文提出了一种基于实体分辨率的方法,使用户能够在不影响其隐私的情况下揭示其移动性信息,即使这些数据与外部公开可用信息相关联。我们使用四个真实数据集提出了详细的实验结果,以说明我们的方法是实用,高效的,通过消除数据之间的潜在链接来实现隐私。

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