Practical Trajectory Anonymization Method Using Latent Space Generalization

The global positioning system (GPS) data are commonly used for location-based services such as traffic flow prediction. However, such data contain considerable sensitive information and thus, they must be anonymized before being published. In this study, we investigate trajectory anonymization. Previous methods have limitations in that they cannot be applied for different load network sparseness and cannot preserve the trajectory information. Thus, we propose a DNN-based method that can anonymize trajectories with different load network sparseness and also preserve the trajectory information. Specifically, the trajectories are projected to the latent space using the pre-trained encoder-decoder model, and the latent variables are generalized. Furthermore, to reduce the information loss, we propose a segment-aware trajectory modeling and study the effectiveness of assuming the normal distribution to the latent space. The experimental results using real GPS data show the effectiveness of the proposed method, presenting the improvement in the data reservation rate by approximately 3% and reducing the reconstruction error by approximately 31%.