  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.
