Hiding Mobile Traffic Fingerprints with GLOVE

Abstract : Preservation of user privacy is paramount in the publication of datasets that contain fine-grained information about individuals. The problem is especially critical in the case of mobile traffic datasets collected by cellular operators, as they feature high subscriber trajectory uniqueness and they are resistant to anonymization through spatiotemporal generalization. In this work, we first unveil the reasons behind such undesirable features of mobile traffic datasets, by leveraging an original measure of the anonymizability of users’ mo- bile fingerprints. Building on such findings, we propose GLOVE, an algorithm that grants k-anonymity of trajectories through specialized generalization. We evalu- ate our methodology on two nationwide mobile traffic datasets, and show that it achieves k-anonymity while preserving a substantial level of accuracy in the data.
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https://hal.inria.fr/hal-01237032
Contributor : Marco Fiore <>
Submitted on : Wednesday, December 2, 2015 - 3:35:49 PM
Last modification on : Tuesday, November 19, 2019 - 12:34:25 PM

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  • HAL Id : hal-01237032, version 1

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Marco Gramaglia, Marco Fiore. Hiding Mobile Traffic Fingerprints with GLOVE. CoNEXT 2015 - ACM 11th International Conference on emerging Networking EXperiments and Technologies, Dec 2015, Heidelberg, Germany. ⟨hal-01237032⟩

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