TRAP-Guard: A Road Network-Constrained Privacy Protection Framework for Multi-modal Trajectories

The rapid development of multimodal transportation poses dual challenges for secure cross-modal trajectory data management. Addressing the core issues of spatial structure distortion and spatiotemporal fragmentation in existing methods, which result in reduced usability and credibility of the generated trajectories. This paper proposes Trajectory Rule-Aware Privacy Guardian(TRAP-Guard), a dual-layer road network-constrained privacy preservation model. The model utilizes Sequential Path Variational Autoencoder (SeqPathVAE) based on Variational AutoEncoder (VAE) architecture with path completion to effectively improve the spatial topological consistency of the generated trajectories. During the trajectory point-level perturbation phase, by integrating the global candidate set of the road network, the exponential mechanism, and temporal constraints, fine-grained completion satisfying Graph-Geo-Indistinguishability (GGI) is achieved. Experimental validation on real-world public transportation datasets confirms that TRAP-Guard significantly enhances spatial rationality, temporal consistency, and overall utility of privacy-preserved trajectories.