  Generating human mobility trajectories is of great importance to solve the
lack of large-scale trajectory data in numerous applications, which is caused
by privacy concerns. However, existing mobility trajectory generation methods
still require real-world human trajectories centrally collected as the training
data, where there exists an inescapable risk of privacy leakage. To overcome
this limitation, in this paper, we propose PateGail, a privacy-preserving
imitation learning model to generate mobility trajectories, which utilizes the
powerful generative adversary imitation learning model to simulate the
decision-making process of humans. Further, in order to protect user privacy,
we train this model collectively based on decentralized mobility data stored in
user devices, where personal discriminators are trained locally to distinguish
and reward the real and generated human trajectories. In the training process,
only the generated trajectories and their rewards obtained based on personal
discriminators are shared between the server and devices, whose privacy is
further preserved by our proposed perturbation mechanisms with theoretical
proof to satisfy differential privacy. Further, to better model the human
decision-making process, we propose a novel aggregation mechanism of the
rewards obtained from personal discriminators. We theoretically prove that
under the reward obtained based on the aggregation mechanism, our proposed
model maximizes the lower bound of the discounted total rewards of users.
Extensive experiments show that the trajectories generated by our model are
able to resemble real-world trajectories in terms of five key statistical
metrics, outperforming state-of-the-art algorithms by over 48.03%. Furthermore,
we demonstrate that the synthetic trajectories are able to efficiently support
practical applications, including mobility prediction and location
recommendation.
