A Predictive Differentially-Private Mechanism for Mobility Traces

  • Konstantinos Chatzikokolakis
  • Catuscia Palamidessi
  • Marco Stronati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8555)


With the increasing popularity of GPS-enabled handheld devices, location based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their millions of users. Trying to address these issues, the notion of geo-indistinguishability was recently introduced, adapting the well-known concept of Differential Privacy to the area of location-based systems. A Laplace-based obfuscation mechanism satisfying this privacy notion works well in the case of a sporadic use; Under repeated use, however, independently applying noise leads to a quick loss of privacy due to the correlation between the location in the trace.

In this paper we show that correlations in the trace can be in fact exploited in terms of a prediction function that tries to guess the new location based on the previously reported locations. The proposed mechanism tests the quality of the predicted location using a private test; in case of success the prediction is reported otherwise the location is sanitized with new noise. If there is considerable correlation in the input trace, the extra cost of the test is small compared to the savings in budget, leading to a more efficient mechanism.

We evaluate the mechanism in the case of a user accessing a location-based service while moving around in a city. Using a simple prediction function and two budget spending strategies, optimizing either the utility or the budget consumption rate, we show that the predictive mechanism can offer substantial improvements over the independently applied noise.


Prediction Function Prediction Rate Independent Mechanism Location Privacy Predictive Component 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Konstantinos Chatzikokolakis
    • 1
  • Catuscia Palamidessi
    • 1
  • Marco Stronati
    • 1
  1. 1.CNRS, INRIA, LIX Ecole PolytechniqueFrance

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