Location Privacy in Relation to Trusted Peers

  • Klaus Rechert
  • Benjamin Greschbach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7170)


One common assumption when defining location privacy metrics is that one is dealing with attackers who have the objective of re-identifying an individual out of an anonymized data set. However, in today’s communication scenarios, user communication and information exchange with (partially) trusted peers is very common, e.g., in communication via social applications. When disclosing voluntarily a single observation to a (partially) trusted communication peer, the user’s privacy seems to be unharmed. However, location data is able to transport much more information than the simple fact of a user being at a specific location. Hence, a user-centric privacy metric is required in order to measure the extent of exposure by releasing (a set of) location observations. The goal of such a metric is to enable individuals to estimate the privacy loss caused by disclosing further location information in a specific communication scenario and thus enabling the user to make informed choices, e.g., choose the right protection mechanism.


Location Information Information Gain Location Privacy Location Observation Adversary Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Klaus Rechert
    • 1
  • Benjamin Greschbach
    • 2
  1. 1.Faculty of EngineeringAlbert-Ludwigs UniversityFreiburg i. B.Germany
  2. 2.School of Computer Science and CommunicationKTH - Royal Institute of TechnologyStockholmSweden

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