We propose a novel technique to ensure location privacy for mobility data using differential privacy. Privacy is guaranteed through path perturbation by injecting noise to both the space and time domain of a spatio-temporal data. In addition, we present to the best of our knowledge, the first context aware differential private algorithm. We conducted numerous experiments on real and synthetic datasets, and show that our approach produces superior privacy results when compared to state-of-the-art techniques.


Spatial Database LBS Differential Privacy 


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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Roland Assam
    • 1
  • Marwan Hassani
    • 1
  • Thomas Seidl
    • 1
  1. 1.RWTH Aachen UniversityGermany

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