Re-identificação de trajetórias de veículos baseada na caracterização das preferências de caminho
Vehicular mobility traces are datasets of vehicles location in a region with high spatiotemporal precision. Access to this sensitive information can threaten the safety and privacy of drivers, such as analyzing this data makes it possible to discover other contextual and latent information, such as users daily home routes or workplaces address. In this way, many obfuscation and anonymization techniques have been proposed to mitigate the problem of user location privacy. In this work, we analyze an anonymization technique called mix-zone, where selected urban regions promote the simultaneous anonymization of vehicles by changing their pseudonym. We show how information about drivers behavior in a city, such as their road preferences, can be used to re-identify their trajectories. We present a simple and efficient re-identification technique that uses only two geo-referenced points as input data. We validate our technique with a real dataset of taxi cabs, being able to reidentify up to 95% of anonymised trajectories.
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