A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities

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.

Key Contributions

New utility evaluation framework for synthetic trajectory generators
Novel membership inference attack against trajectory generative models previously considered private
Demonstration that resistance to trajectory user-linking does not guarantee privacy against membership inference

🛡️ Threat Analysis

Membership Inference Attack

Paper proposes a new membership inference attack against synthetic trajectory generators to determine if specific trajectories were in the training data.