PateGAIL++: Utility Optimized Private Trajectory Generation with Imitation Learning

Human mobility trajectory data supports a wide range of applications, including urban planning, intelligent transportation systems, and public safety monitoring. However, large-scale, high-quality mobility datasets are difficult to obtain due to privacy concerns. Raw trajectory data may reveal sensitive user information, such as home addresses, routines, or social relationships, making it crucial to develop privacy-preserving alternatives. Recent advances in deep generative modeling have enabled synthetic trajectory generation, but existing methods either lack formal privacy guarantees or suffer from reduced utility and scalability. Differential Privacy (DP) has emerged as a rigorous framework for data protection, and recent efforts such as PATE-GAN and \textsc{PateGail} integrate DP with generative adversarial learning. While promising, these methods struggle to generalize across diverse trajectory patterns and often incur significant utility degradation. In this work, we propose a new framework that builds on \textsc{PateGail\texttt{++}} by introducing a \emph{sensitivity-aware noise injection module} that dynamically adjusts privacy noise based on sample-level sensitivity. This design significantly improves trajectory fidelity, downstream task performance, and scalability under strong privacy guarantees. We further adapt our framework to the local differential privacy (LDP) setting, allowing individual-level protection without reliance on a trusted server. We evaluate our method on a real-world mobility dataset and demonstrate its superiority over state-of-the-art baselines in terms of privacy-utility trade-off.