Realtime location privacy via mobility prediction: creating confusion at crossroads

ABSTRACT

Users face privacy risks when providing personal location data to untrusted location based services (LBSs). Many existing attempts to anonymize users have relied on suppressing information about users' locations. This suppression results in reduced functionality, including degraded spatial accuracy or the loss of realtime location updates. We propose anonymizing a user by camouflaging their current location with various predicted paths. This is akin to increasing the noise floor around the user. We believe this different approach provides many advantages. By allowing the user to keep all desired functionality while still protecting the user's privacy, we enable use of a broader class of LBSs. To demonstrate our proposal, we develop CacheCloak, a system that enables realtime anonymization of continuous location updates without any loss of accuracy. Data from a GIS-based traffic simulation in an urban environment shows that CacheCloak can anonymize users rapidly.

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INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.4 COMPUTERS AND SOCIETY
      K.4.1 Public Policy Issues
          Subjects: Privacy

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.m Miscellaneous

  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.5 Security and Protection (D.4.6, K.4.2)

General Terms:
Algorithms, Performance, Security

Keywords:
GPS, LBS, anonymity, location privacy, mobility prediction