SoK: Differentially Private Publication of Trajectory Data

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users' paths is extremely privacy-invasive. Therefore, there a necessity protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult protect, since they sequential, highly dimensional, correlated, bound geophysical restrictions, easily mapped semantic points interest. This paper aims establish systematic framework on protective masking synthetic-generation measures trajectory databases with syntactic differentially (DP) guarantees, including also utility derived ideas limitations existing proposals. To reach this goal, systematize metrics used throughout literature, deeply analyze DP granularity notions, explore elaborate state art privacy-enhancing mechanisms their problems, expose main notions context trajectories.