Human mobility data are used in numerous applications, ranging from public health to urban planning. Human mobility is inherently sensitive, as it can contain information such as religious beliefs and political affiliations. Historically, it has been proposed to modify the information using techniques such as aggregation, obfuscation, or noise addition, to adequately protect privacy and eliminate concerns. As these methods come at a great cost in utility, new methods leveraging development in generative models, were introduced. The extent to which such methods answer the privacy-utility trade-off remains an open problem. In this paper, we introduced a first step towards solving it, by the introduction and application of a new framework for utility evaluation. Furthermore, we provide evidence that privacy evaluation remains a great challenge to consider and that it should be tackled through adversarial evaluation in accordance with the current EU regulation. We propose a new membership inference attack against a subcategory of generative models, even though this subcategory was deemed private due to its resistance over the trajectory user-linking problem.

翻译：人类移动数据被广泛应用于从公共卫生到城市规划的众多领域。由于移动数据可能包含宗教信仰和政治倾向等信息，其本质上是敏感的。历史上，研究者提出通过聚合、混淆或添加噪声等技术来修改信息，以充分保护隐私并消除顾虑。由于这些方法会极大损害数据效用，利用生成模型发展的新方法应运而生。这类方法在多大程度上能够平衡隐私与效用仍是一个悬而未决的问题。本文通过引入并应用新的效用评估框架，迈出了解决该问题的第一步。此外，我们提供了证据表明，隐私评估仍是一个需要高度重视的挑战，且应根据现行欧盟法规通过对抗性评估加以解决。我们针对生成模型的一个子类别提出了一种新的成员推理攻击——尽管该子类别因对轨迹用户链接问题具有抵抗力而被视为具备隐私保护能力。