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Open AccessArticle

Entropy-Based Privacy against Profiling of User Mobility

Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Madrid, Spain
Department of Telematics Engineering, Universitat Politècnica de Catalunya (UPC), Campus Nord, C. Jordi Girona 1-3, 08034 Barcelona, Spain
Computer Science Department, Missouri University of Science and Technology, 325B Computer Science Building, Rolla, MO 65409-0350, USA
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Entropy 2015, 17(6), 3913-3946;
Received: 9 March 2015 / Revised: 29 May 2015 / Accepted: 8 June 2015 / Published: 10 June 2015
Location-based services (LBSs) flood mobile phones nowadays, but their use poses an evident privacy risk. The locations accompanying the LBS queries can be exploited by the LBS provider to build the user profile of visited locations, which might disclose sensitive data, such as work or home locations. The classic concept of entropy is widely used to evaluate privacy in these scenarios, where the information is represented as a sequence of independent samples of categorized data. However, since the LBS queries might be sent very frequently, location profiles can be improved by adding temporal dependencies, thus becoming mobility profiles, where location samples are not independent anymore and might disclose the user’s mobility patterns. Since the time dimension is factored in, the classic entropy concept falls short of evaluating the real privacy level, which depends also on the time component. Therefore, we propose to extend the entropy-based privacy metric to the use of the entropy rate to evaluate mobility profiles. Then, two perturbative mechanisms are considered to preserve locations and mobility profiles under gradual utility constraints. We further use the proposed privacy metric and compare it to classic ones to evaluate both synthetic and real mobility profiles when the perturbative methods proposed are applied. The results prove the usefulness of the proposed metric for mobility profiles and the need for tailoring the perturbative methods to the features of mobility profiles in order to improve privacy without completely loosing utility. View Full-Text
Keywords: location-based services (LBSs); entropy; privacy; perturbative methods; location history location-based services (LBSs); entropy; privacy; perturbative methods; location history
MDPI and ACS Style

Rodriguez-Carrion, A.; Rebollo-Monedero, D.; Forné, J.; Campo, C.; Garcia-Rubio, C.; Parra-Arnau, J.; Das, S.K. Entropy-Based Privacy against Profiling of User Mobility. Entropy 2015, 17, 3913-3946.

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