Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Spatial Anonymity

  • Panos Kalnis
  • Gabriel Ghinita
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_352


Anonymity in location-based services;Privacy-preserving spatial queries;Spatial k-anonymity


LetU be a user who is asking via a mobile device (e.g., phone, PDA) a query relevant to his current location, such as “find the nearest betting office.” This query can be answered by a Location Based Service (LBS) in a public web server (e.g., Google Maps, MapQuest), which is not trustworthy. Since the query may be sensitive, U uses encryption and a pseudonym, in order to protect his privacy. However, the query still contains the exact location, which may reveal the identity of U. For example, if U asks the query within his residence, an attacker may use public information (e.g., white pages) to associate the location with U. Spatial k-Anonymity (SKA) solves this problem by ensuring that an attacker cannot identify U as the querying user with probability larger than 1∕k, where kis a user-defined anonymity requirement. To achieve this, a centralized or distributed...

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Panos Kalnis
    • 1
  • Gabriel Ghinita
    • 1
  1. 1.National University of SingaporeSingaporeSingapore

Section editors and affiliations

  • Dimitris Papadias
    • 1
  1. 1.Dept. of Computer Science and Eng.Hong Kong Univ. of Science and TechnologyKowloonHong Kong SAR