While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research.

SoK: Can Trajectory Generation Combine Privacy and Utility?

Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introduce the Hierarchical and Multi-Resolution Network (HRNet), a novel deep generative model specifically designed to synthesize realistic human mobility data while guaranteeing differential privacy. We first identify the key difficulties inherent in learning human mobility data under differential privacy. In response to these challenges, HRNet integrates three components: a hierarchical location encoding mechanism, multi-task learning across multiple resolutions, and private pre-training. These elements collectively enhance the model's ability under the constraints of differential privacy. Through extensive comparative experiments utilizing a real-world dataset, HRNet demonstrates a marked improvement over existing methods in balancing the utility-privacy trade-off. 

pdf/hrnet-differentially-private-hierarchical-and-multi-3k2nklkdx8.pdf

HRNet: Differentially Private Hierarchical and Multi-Resolution Network
  for Human Mobility Data Synthesization

Human mobility modeling is crucial for many facets of our society, including disease transmission modeling and urban planning. The explosion of mobility data prompted the application of deep learning to human mobility. Along with the growth of research interest, there is also increasing privacy concern. This study first examines the cutting-edge approaches for trajectory generation, classification, and next-location prediction. Second, we propose a novel privacy-aware approach for predicting next-week trajectories. The approach is based on two modules, a Generative Adversarial Network used for generating synthetic trajectories and a deep learning model for user identification which safeguards privacy. These two modules are combined with a next-week trajectory predictor that uses privacy-aware synthetic data. The experiments on two real-life datasets show that the generator creates trajectories similar to the real ones yet different enough to safeguard privacy. The low user-recognition recognition accuracy of models trained on the generated data demonstrates privacy awareness. Statistical tests confirm no significant difference between the original and the generated trajectories. We further demonstrate the utility of the synthetic data by predicting week-ahead trajectories based on the synthetic trajectories. Our study shows how privacy and utility can be managed jointly using the proposed privacy-aware approach.

GANs for Privacy-Aware Mobility Modeling

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GANs for Privacy-Aware Mobility Modeling