Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data

By Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, Depeng Jin. 2017

203 citations recorded by the source corpus.

Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual's mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users' privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals' trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual's trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets. Through the investigation on aggregated mobility data, our work recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both academy and industry.

Subfields: Artificial Intelligence, Sociology and Political Science, Transportation

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Citations in the corpus

Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “[36] present an attack that exploits the uniqueness and the regularity of human mobility, and extracts location trajectories from aggregate mobility data.”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “[50] have recently presented an attack that recovers individual users’ trajectories from aggregate mobility data, by exploiting the uniqueness and the regularity of human mobility.”
Narrow Band Internet of Things (2017, background): “From the perspective of frequency spectrum licensing, LPWAN technologies can be classified into two categories, technologies that work in unauthorized spectrum and technologies that work in authorized spectrum [7], [8].”
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, methodology): “Pattern-based methods: Pattern-based methods [25, 26, 34, 40, 42] first discover the popular sequential mobility patterns from the trajectory, and then try to predict the mobility based on these popular patterns.”