Title: $k^{τ,ε}$-anonymity: Towards Privacy-Preserving Publishing of Spatiotemporal Trajectory Data,
Abstract: Mobile network operators can track subscribers via passive or active
monitoring of device locations. The recorded trajectories offer an
unprecedented outlook on the activities of large user populations, which
enables developing new networking solutions and services, and scaling up
studies across research disciplines. Yet, the disclosure of individual
trajectories raises significant privacy concerns: thus, these data are often
protected by restrictive non-disclosure agreements that limit their
availability and impede potential usages. In this paper, we contribute to the
development of technical solutions to the problem of privacy-preserving
publishing of spatiotemporal trajectories of mobile subscribers. We propose an
algorithm that generalizes the data so that they satisfy
$k^{\tau,\epsilon}$-anonymity, an original privacy criterion that thwarts
attacks on trajectories. Evaluations with real-world datasets demonstrate that
our algorithm attains its objective while retaining a substantial level of
accuracy in the data. Our work is a step forward in the direction of open,
privacy-preserving datasets of spatiotemporal trajectories.