Deep Learning-Based Synthetic Trajectory Generation for Enhanced Privacy and Utility

With the increasing prominence of trajectory data privacy and security issues, trajectory data privacy protection algorithms based on deep learning have received widespread attention. The core idea of such algorithms is to use synthetic trajectories to replace real trajectories for publishing. However, the generated synthetic trajectories may deviate from the real trajectories excessively, thus affecting the utility of trajectory data. Aiming at the above problems, the MogLSTM-TrajGAN trajectory data privacy protection algorithm is proposed in this paper. Firstly, encoding the real trajectory data to obtain the spatio-temporal distribution of the real trajectories and adding the gating unit on the traditional long short-term memory network to establish a richer interaction space between the input and the output. Then, the improved long and short-term memory network is combined with the generative adversarial network to generate synthetic trajectories. Finally, the trajectory loss function is designed to measure the similarity loss between the synthetic trajectory data and the real trajectory data. Simulation results show that the trajectory data generated by the proposed algorithm retains the spatial, temporal and point-of-interest features of the real trajectory data, and effectively balances the privacy and utility of the trajectory data.