AnonySense: Opportunistic and Privacy-Preserving Context Collection

  • Authors:

  • Apu Kapadia;Nikos Triandopoulos;Cory Cornelius;Daniel Peebles;David Kotz

  • Affiliations:

  • Institute for Security Technology Studies, Dartmouth College, Hanover, USA NH 03755;Department of Computer Science, University of Aarhus, Aarhus N, Denmark 8200;Institute for Security Technology Studies, Dartmouth College, Hanover, USA NH 03755;Institute for Security Technology Studies, Dartmouth College, Hanover, USA NH 03755;Institute for Security Technology Studies, Dartmouth College, Hanover, USA NH 03755

  • Venue:
  • Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
  • Year:
  • 2009

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Abstract

Opportunistic sensing allows applications to "task" mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street, or users' mobile phones to locate (Bluetooth-enabled) objects in their neighborhood. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk--even if a report has been anonymized, the accompanying time and location can reveal sufficient information to deanonymize the user whose device sent the report.We propose AnonySense, a general-purpose architecture for leveraging users' mobile devices for measuring context, while maintaining the privacy of the users.AnonySense features multiple layers of privacy protection--a framework for nodes to receive tasks anonymously, a novel blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context, and k-anonymous report aggregation to improve the users' privacy against applications receiving the context. We outline the architecture and security properties of AnonySense, and focus on evaluating our tessellation and clustering algorithm against real mobility traces.