Putting people in their place: an anonymous and privacy-sensitive approach to collecting sensed data in location-based applications

  • Authors:

  • Karen P. Tang;Pedram Keyani;James Fogarty;Jason I. Hong

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2006

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Abstract

The emergence of location-based computing promises new and compelling applications, but raises very real privacy risks. Existing approaches to privacy generally treat people as the entity of interest, often using a fidelity tradeoff to manage the costs and benefits of revealing a person's location. However, these approaches cannot be applied in some applications, as a reduction in precision can render location information useless. This is true of a category of applications that use location data collected from multiple people to infer such information as whether there is a traffic jam on a bridge, whether there are seats available in a nearby coffee shop, when the next bus will arrive, or if a particular conference room is currently empty. We present hitchhiking, a new approach that treats locations as the primary entity of interest. Hitchhiking removes the fidelity tradeoff by preserving the anonymity of reports without reducing the precision of location disclosures. We can therefore support the full functionality of an interesting class of location-based applications without introducing the privacy concerns that would otherwise arise.