GeoGRCNN: Synthetic Trajectory Generation for Location Privacy Protection

In light of the growing prevalence of location-based applications, a substantial amount of location data has been accumulated, facilitating various services and research endeavors such as traffic flow analysis, identification of similar movement patterns, and detection of social circles. This surge in data usage has, however, prompted heightened concerns about location privacy among users. Our work aims to fortify users' location privacy without compromising the advantages brought by location-based services. Specifically, we propose an innovative deep learning model called GeoGRCNN which is capable of generating synthetic trajectories indistinguishable from real human trajectories. Such synthetic trajectories can then be mixed with real human trajectories to serve as decoys that prevent users from being profiled by the service provider. Our model seamlessly combines Graph Convolutional Networks(GCN) and Recurrent Neural Networks(RNN) to faithfully replicate authentic movement patterns within urban environments. Our evaluation focuses on measuring the spatial accuracy of these trajectories and their conformity to actual road networks. The experimental results demonstrate that our model effectively replicates actual urban layouts with minimal deviations from road networks. We achieve trajectory generation accuracy of 95.06 % for Beijing and 97.37% for Atlanta.