PathForge: faithful anonymization of movement data

  • Authors:
  • Sebastian Kay Belle;Marcel Waldvogel;Oliver Haase

  • Affiliations:
  • University of Konstanz, Konstanz, Germany;University of Konstanz, Konstanz, Germany;Konstanz University of Applied Sciences, Konstanz, Germany

  • Venue:
  • Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds
  • Year:
  • 2009

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Abstract

For most mobile networks, providers need the current position of their users to provide efficient service. The resulting motion data is not only an invaluable source for analyzing traffic or flow patterns, but also for tracking an individual's whereabouts, even without their knowledge. Today, many carry at least one mobile networked device with them wherever they go, day and night. The resulting motion data can be used to reveal the most intimate details of our lives, making this information extremely privacy sensitive. In this paper, we present PathForge, a lightweight solution, which not only fulfills the provider's efficiency requirement, but continues to allow flow pattern analysis, yet provides full privacy for users when not actively involved in a call.