Due to the popularity of mobile computing and mobile sensing, users’ traces can now be readily collected to enhance applications’ performance. However, users’ location privacy may be disclosed to the untrusted data aggregator that collects users’ traces. Cloaking users’ traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose $W^{3}{-}tess$ that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of $W^{3}{-}tess$ is to credibly imitate the temporal, spatial, and social behavior of users’ mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, $W^{3}{-}tess$ can provide “ differential privacy ” on location privacy preservation. In addition, compared to the existing work, $W^{3}{-}tess$ offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of $W^{3}{-}tess$ .

中文翻译：

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合成隐私保留痕迹：通过社交网络增强可信度

由于移动计算和移动感应的普及，现在可以轻松收集用户的踪迹以增强应用程序的性能。但是，用户的位置隐私可能会透露给收集用户跟踪的不可信数据聚合器。用合成踪迹掩盖用户的踪迹是一种保护位置隐私的流行技术。但是，合成痕迹的现有工作遭受了基于社会关系的去匿名化攻击。为此，我们建议 $ W ^ {3} {-} tess $ 通过增强功能来合成隐私保护痕迹 合理性社会网络的合成痕迹。的主要思想 $ W ^ {3} {-} tess $ 目的是可靠地模仿用户移动的时间，空间和社交行为，对表现出类似三维移动行为的轨迹进行采样，并使用采样位置来合成轨迹。通过这样做， $ W ^ {3} {-} tess $ 可以提供 ” 差异隐私 在位置隐私保护上。此外，与现有工作相比， $ W ^ {3} {-} tess $ 提供了几个突出的功能。首先，位置隐私保护和数据实用性保证在理论上都是可以证明的。其次，它适用于由数据聚合器执行的大多数地理数据分析任务。在两个真实世界的数据集loc-Gwalla和loc-Brightkite上进行的实验表明，该方法的有效性和效率 $ W ^ {3} {-} tess $ 。