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Preserving Mobile Subscriber Privacy in Open Datasets of Spatiotemporal Trajectories

Gramaglia, Marco and Fiore, Marco and Tarable, Alberto and Banchs, Albert (2017) Preserving Mobile Subscriber Privacy in Open Datasets of Spatiotemporal Trajectories. In: The 36th IEEE International Conference on Computer Communications (IEEE INFOCOM 2017), 1-4 May 2017, Atlanta, GA, USA.

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Abstract

Mobile network operators can track subscribers via passive or active monitoring of device locations. The recorded trajectories offer an unprecedented outlook on the activities of large user populations, which enables developing new networking solutions and services, and scaling up studies across research disciplines. Yet, the disclosure of individual trajectories raises significant privacy concerns: thus, these data are often protected by restrictive non-disclosure agreements that limit their availability and impede potential usages. In this paper, we contribute to the development of technical solutions to the problem of privacy-preserving publishing of spatiotemporal trajectories of mobile subscribers. We propose an algorithm that generalizes the data so that they satisfy kτ,ǫ-anonymity, an original privacy criterion that thwarts attacks on trajectories. Evaluations with real-world datasets demonstrate that our algorithm attains its objective while retaining a substantial level of accuracy in the data. Our work is a step forward in the direction of open, privacy-preserving datasets of spatiotemporal trajectories.

Item Type: Conference or Workshop Papers (Paper)
Subjects: UNSPECIFIED
Divisions: UNSPECIFIED
Depositing User: Rebeca De Miguel
Date Deposited: 12 May 2017 11:03
Last Modified: 12 May 2017 11:03
URI: http://eprints.networks.imdea.org/id/eprint/1591

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