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Entropy 2015, 17(6), 3913-3946; doi:10.3390/e17063913

Entropy-Based Privacy against Profiling of User Mobility

1
Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Madrid, Spain
2
Department of Telematics Engineering, Universitat Politècnica de Catalunya (UPC), Campus Nord, C. Jordi Girona 1-3, 08034 Barcelona, Spain
3
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
Received: 9 March 2015 / Revised: 29 May 2015 / Accepted: 8 June 2015 / Published: 10 June 2015
View Full-Text   |   Download PDF [545 KB, uploaded 10 June 2015]   |  

Abstract

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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