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Trajectory datasets are becoming more and more popular due to the massive usage of GPS and other location-based devices and services. In this paper, the authors address privacy issues regarding the identification of individuals in static trajectory datasets. They provide privacy protection by defining trajectory k-anonymity, meaning every released information refers to at least k users/trajectories. They propose a novel generalization-based approach that applies to trajectories and sequences in general. They also suggest the use of a simple random reconstruction of the original dataset from the anonymization, to overcome possible drawbacks of generalization approaches.
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