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\begin{document}

\title{A Survey and Experimental Study on Privacy-Preserving Trajectory Data
Publishing}



\author[1]{Fengmei Jin }%
\author[2]{Wen Hua }%
\author[2]{Matteo Francia }%
\author[2]{Pingfu Chao }%
\author[2]{Maria Orlowska }%
\author[2]{Xiaofang Zhou }%
\affil[1]{The University of Queensland}%
\affil[2]{Affiliation not available}%


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  \date{June 14, 2024}


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\begin{abstract}
Trajectory data has become ubiquitous nowadays, which can benefit
various real-world applications such as traffic management and
location-based services. However, trajectories may disclose highly
sensitive information of an individual including mobility patterns,
personal profiles and gazetteers, social relationships, etc, making it
indispensable to consider privacy protection when releasing trajectory
data. Ensuring privacy on trajectories demands more than hiding single
locations, since trajectories are intrinsically sparse and
high-dimensional, and require to protect multi-scale correlations. To
this end, extensive research has been conducted to design effective
techniques for privacy-preserving trajectory data publishing.
Furthermore, protecting privacy requires carefully balance two metrics:
privacy and utility. In other words, it needs to protect as much privacy
as possible and meanwhile guarantee the usefulness of the released
trajectories for data analysis. In this survey, we provide a
comprehensive study and systematic summarization of existing protection
models, privacy and utility metrics for trajectories developed in the
literature. We also conduct extensive experiments on a real-life public
trajectory dataset to evaluate the performance of several representative
privacy protection models, demonstrate the trade-off between privacy and
utility, and guide the choice of the right privacy model for trajectory
publishing given certain privacy and utility desiderata.%
\end{abstract}%



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