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。

SoK: 轨迹生成是否可以兼顾隐私和效用？

SoK: Can Trajectory Generation Combine Privacy and Utility?

Nowadays, mobile devices enable constant tracking of the user's position and
location trajectories can be used to infer personal points of interest (POIs)
like homes, workplaces, or stores. A common way to extract POIs is to first
identify spatio-temporal regions where a user spends a significant amount of
time, known as stay regions (SRs).
Common approaches to SR extraction are evaluated either solely unsupervised
or on a small-scale private dataset, as popular public datasets are unlabeled.
Most of these methods rely on hand-crafted features or thresholds and do not
learn beyond hyperparameter optimization. Therefore, we propose a weakly and
self-supervised transformer-based model called DeepStay, which is trained on
location trajectories to predict stay regions. To the best of our knowledge,
this is the first approach based on deep learning and the first approach that
is evaluated on a public, labeled dataset. Our SR extraction method outperforms
state-of-the-art methods. In addition, we conducted a limited experiment on the
task of transportation mode detection from GPS trajectories using the same
architecture and achieved significantly higher scores than the
state-of-the-art. Our code is available at
this https URL

本文介绍了一种基于深度学习的弱监督和自监督的 Transformer 模型，称为 DeepStay，用于从位置轨迹中提取停留区域，同时在交通方式检测任务中取得显著的成绩。