  Researchers face the trade-off between publishing mobility data along with
their papers while simultaneously protecting the privacy of the individuals. In
addition to the fundamental anonymization process, other techniques, such as
spatial discretization and, in certain cases, location concealing or complete
removal, are applied to achieve these dual objectives. The primary research
question is whether concealing the observation area is an adequate form of
protection or whether human mobility patterns in urban areas are inherently
revealing of location. The characteristics of the mobility data, such as the
number of activity records or the number of unique users in a given spatial
unit, reveal the silhouette of the urban landscape, which can be used to infer
the identity of the city in question. It was demonstrated that even without
disclosing the exact location, the patterns of human mobility can still reveal
the urban area from which the data was collected. The presented locating method
was tested on other cities using different open data sets and against coarser
spatial discretization units. While publishing mobility data is essential for
research, it was demonstrated that concealing the observation area is
insufficient to prevent the identification of the urban area. Furthermore,
using larger discretization units alone is an ineffective solution to the
problem of the observation area re-identification. Instead of obscuring the
observation area, noise should be added to the trajectories to prevent user
identification.
