SoK: Can Trajectory Generation Combine Privacy and Utility?
Erik Buchholz Alsharif Abuadbba Shuo Wang Surya Nepal Salil S. Kanhere
While location trajectories represent a valuable data source for analyses and
location-based services, they can reveal sensitive information, such as
political and religious preferences. Differentially private publication
mechanisms have been proposed to allow for analyses under rigorous privacy
guarantees. However, the traditional protection schemes suffer from a limiting
privacy-utility trade-off and are vulnerable to correlation and reconstruction
attacks. Synthetic trajectory data generation and release represent a promising
alternative to protection algorithms. While initial proposals achieve
remarkable utility, they fail to provide rigorous privacy guarantees. This
paper proposes a framework for designing a privacy-preserving trajectory
publication approach by defining five design goals, particularly stressing the
importance of choosing an appropriate Unit of Privacy. Based on this framework,
we briefly discuss the existing trajectory protection approaches, emphasising
their shortcomings. This work focuses on the systematisation of the
state-of-the-art generative models for trajectories in the context of the
proposed framework. We find that no existing solution satisfies all
requirements. Thus, we perform an experimental study evaluating the
applicability of six sequential generative models to the trajectory domain.
Finally, we conclude that a generative trajectory model providing semantic
guarantees remains an open research question and propose concrete next steps
for future research.