Abstract
Regardless of the collection location, mobile traffic data contains information about many aspects of subscribers’ lives, including their activities, interests, schedules, travel and preferences. It is precisely the ability to access such information on unprecedented scales that is of critical importance for studies in a wide variety of fields. However, access to such a rich source also raises concerns about potential infringements on the rights of mobile customers regarding their personal data: among other things, individuals can be identified, their movements can be modified, their movements can be tracked and their mobile stage fright can be monitored. As a result, regulators have been working on legislation to protect the privacy of mobile users. In this optic, we provide a scalable solution to anonymize Big Data Spatio-temporal Trajectories of mobile users.
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Notes
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For the sake of anonimity of the company owning the data set.
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Eddine, H.J. (2022). Scalable Solution for the Anonymization of Big Data Spatio-Temporal Trajectories. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_32
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