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.

定义五个设计目标的隐私保护轨迹发布方法，以选择适当的隐私单元为重点。在此框架下，对现有的轨迹保护方法进行了简要讨论，并强调它们的不足之处。本工作聚焦于在所提出的框架下对轨迹的最新生成模型进行系统化。我们发现目前没有现有的解决方案能够满足所有需求。因此，我们进行了一个实验研究，评估六个顺序生成模型在轨迹领域的适用性。最后，我们得出结论，提供语义保证的生成轨迹模型仍然是一个开放的研究问题，并提出了未来研究的具体下一步。