From itemsets through trajectories to location based services: a knowledge hiding privacy approach
ACM SIGKDD Explorations Newsletter
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The widespread adoption of Location Based Services (LBSs) coupled with recent advances in location tracking technologies, pose serious concerns to user privacy. As a consequence, privacy preserving approaches have been proposed to protect the location information which is communicated during a request for an LBS. Most existing approaches are centralized as they rely on a trusted server to protect the real location of the user. Although the centralized approaches are commonplace, so far no attempt has been made to integrate them in a unified framework. Such an integration would provide the means for easily implementing and testing new techniques by offering ready–made vanilla system components and allow for both the experimental and analytical evaluation of the implemented techniques.In this paper we propose PLOT, an open–ended toolbox that allows the implementation and the evaluation of privacy–enhancing algorithms for LBSs. PLOT offers a variety of interesting features: (i) it supports both real and synthetic movement data, (ii) it relies on spatial DBMSs to efficiently handle movement data as well as the underlying model of user movement, (iii) it offers tools for mobile data preprocessing, movement reconstruction and segmentation, (iv) it allows the implementation of both network–based and free–terrain solutions to location privacy, (v) it provides the infrastructure for second–chance approaches when the main location privacy approach fails, (vi) it implements strategies for the identification of frequent patterns in user movement, and finally (vii) it offers an extended set of visualization tools that both provide insight on the workings of the implemented solutions and facilitate the qualitative and quantitative evaluation of their behavior.