PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning

TL;DR

PateGail is a privacy-preserving imitation learning model that generates realistic human mobility trajectories using decentralized data and differential privacy, outperforming existing methods and supporting practical applications.

Contribution

The paper introduces PateGail, a novel decentralized, privacy-preserving imitation learning framework for generating human mobility trajectories with theoretical privacy guarantees.

Findings

Generated trajectories closely match real-world data in key statistical metrics.

Outperforms state-of-the-art algorithms by over 48.03%.

Supports practical applications like mobility prediction and location recommendation.

Abstract

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.