Abstract
Releasing and sharing mobility data, and specifically trajectories, is necessary for many applications, from infrastructure planning to epidemiology. Yet, trajectories are highly sensitive data, because the points visited by an individual can be identifying and also confidential. Hence, trajectories must be anonymized before releasing or sharing them. While most contributions to the trajectory anonymization literature take statistical approaches, deep learning is increasingly being used. We observe that natural language sentences and trajectories share a sequential nature that can be exploited in similar ways. In this paper, we present preliminary work on generating synthetic trajectories using machine learning models typically used for natural language processing. Our empirical results attest to the quality of the generated synthetic trajectories. Furthermore, our methods allow discovering natural neighborhoods based on trajectories.
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Notes
- 1.
A SF local’s guide to the neighborhoods of San Francisco. https://sfgal.com/sf-locals-guide-to-neighborhoods-of-san-francisco/.
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Acknowledgements
This research was funded by the European Commission (projects H2020-871042 “SoBigData++” and H2020-101006879 “MobiDataLab”), and the Government of Catalonia (ICREA Acadèmia Prize to J. Domingo-Ferrer, FI grant to N. Jebreel). The authors are with the UNESCO Chair in Data Privacy, but the views in this paper are their own and are not necessarily shared by UNESCO.
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Blanco-Justicia, A., Jebreel, N.M., Manjón, J.A., Domingo-Ferrer, J. (2022). Generation of Synthetic Trajectory Microdata from Language Models. In: Domingo-Ferrer, J., Laurent, M. (eds) Privacy in Statistical Databases. PSD 2022. Lecture Notes in Computer Science, vol 13463. Springer, Cham. https://doi.org/10.1007/978-3-031-13945-1_13
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