Beyond Spatial Privacy: Protecting Trajectories With Spatio-Temporal Differential Privacy

Spatio-temporal trajectories carry identifying information and are vulnerable to privacy breaches. Existing studies predominantly focus on the spatial domain. The temporal aspect remains underexplored, leaving privacy risks unaddressed. This paper highlights these risks by introducing a new trajectory matching model, ST-ATT, which leverages attention-enhanced Long Short-Term Memory (LSTM) to effectively capture the spatio-temporal correlations within trajectories. ST-ATT excels in identifying similar trajectories. To defend against linkage attacks on spatio-temporal trajectories, including advanced models like ST-ATT, we propose a novel Differential Privacy (DP) mechanism specifically designed to address the privacy risks. We reveal that the privacy budget and violation probability for each spatial point explicitly depend on earlier timestamps. The privacy budget can be flexibly redistributed between spatial and temporal domains without compromising overall privacy. This mechanism complies with DP, even when spatio-temporal points are reordered due to perturbation. Experiments show that ST-ATT can accurately identify spatio-temporal trajectories perturbed by the existing DP methods adding noise solely to the spatial domain. The proposed spatio-temporal DP mechanism resists ST-ATT, highlighting the need for considering spatio-temporal correlations to ensure robust privacy protection in spatio-temporal trajectories.