  Preserving user privacy is paramount when it comes to publicly disclosed
datasets that contain fine-grained data about large populations. The problem is
especially critical in the case of mobile traffic datasets collected by
cellular operators, as they feature elevate subscriber trajectory uniqueness
and they are resistant to anonymization through spatiotemporal generalization.
In this work, we investigate the $k$-anonymizability of trajectories in two
large-scale mobile traffic datasets, by means of a novel dedicated measure. Our
results are in agreement with those of previous analyses, however they also
provide additional insights on the reasons behind the poor anonimizability of
mobile traffic datasets. As such, our study is a step forward in the direction
of a more robust dataset anonymization.
