Human mobility trajectories are increasingly collected by ISPs to assist academic research and commercial applications. In this paper, we collected a large-scale ground-truth trajectory dataset from 2,161,500 users of a cellular network, and two matched external trajectory datasets from a large social network (56,683 users) and a check-in/review service (45,790 users) on the same user population. We find that their performance in the real-world dataset is far from the theoretical bound. Further analysis shows that most algorithms have under-estimated the impact of spatio-temporal mismatches between the data from different sources, and the high sparsity of user generated data also contributes to the underperformance. Based on these insights, we propose 4 new algorithms that are specially designed to tolerate spatial or temporal mismatches (or both) and model user behavior. Extensive evaluations show that our algorithms achieve more than 17% performance gain over the best existing algorithms, confirming our insights. Further, we propose 2 new location-privacy protection mechanisms utilizing the spatio-temporal mismatches to better protect users' privacy against the de-anonymization attack. Evaluation results show that our proposed mechanisms can reduce the performance of de-anonymization attacks by over 6.6%.

中文翻译：

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移动轨迹的匿名化和去匿名化：剖析理论与实践之间的差距

ISP 越来越多地收集人类移动轨迹，以协助学术研究和商业应用。在本文中，我们从蜂窝网络的 2,161,500 名用户中收集了一个大规模的真实轨迹数据集，以及来自大型社交网络（56,683 名用户）和签入/评论服务（45,790 名用户）的两个匹配的外部轨迹数据集。在相同的用户群上。我们发现它们在现实世界数据集中的表现远未达到理论界限。进一步的分析表明，大多数算法都低估了不同来源数据之间时空不匹配的影响，而用户生成数据的高度稀疏性也是导致性能不佳的原因。基于这些见解，我们提出了 4 种新算法，专门设计用于容忍空间或时间不匹配（或两者）并为用户行为建模。广泛的评估表明，我们的算法比现有的最佳算法实现了 17% 以上的性能提升，这证实了我们的见解。此外，我们提出了 2 种新的位置隐私保护机制，利用时空不匹配来更好地保护用户隐私免受去匿名化攻击。评估结果表明，我们提出的机制可以将去匿名化攻击的性能降低 6.6% 以上。我们提出了 2 种新的位置隐私保护机制，利用时空不匹配来更好地保护用户隐私免受去匿名化攻击。评估结果表明，我们提出的机制可以将去匿名化攻击的性能降低 6.6% 以上。我们提出了 2 种新的位置隐私保护机制，利用时空不匹配来更好地保护用户隐私免受去匿名化攻击。评估结果表明，我们提出的机制可以将去匿名化攻击的性能降低 6.6% 以上。