A moving-object index for efficient query processing with peer-wise location privacy

  • Authors:

  • Dan Lin;Christian S. Jensen;Rui Zhang;Lu Xiao;Jiaheng Lu

  • Affiliations:

  • Missouri University of Science & Technology;Aarhus University;The University of Melbourne;Missouri University of Science & Technology;Renmin University of China

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2011

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Abstract

With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy---the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a query range, but that do not want to disclose their location to the querying peer. To solve this problem, we propose a novel, privacy-policy enabled index called the PEB-tree that seamlessly integrates location proximity and policy compatibility. We propose efficient algorithms that use the PEB-tree for processing privacy-aware range and kNN queries. Extensive experiments suggest that the PEB-tree enables efficient query processing.