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.
Recommended Citation
A. Rodriguez-Carrion et al., "Entropy-Based Privacy against Profiling of User Mobility," Entropy, vol. 17, no. 6, pp. 3913 - 3946, MDPI AG, Jun 2015.
The definitive version is available at https://doi.org/10.3390/e17063913
Department(s)
Computer Science
Keywords and Phrases
Entropy; Location history; Location-based services (LBSs); Perturbative methods; Privacy
International Standard Serial Number (ISSN)
1099-4300
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2015 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jun 2015
Comments
This work is partially supported by the Spanish Ministry of Science and Innovation through the CONSEQUENCE(TEC2010-20572-C02-01/02) and EMRISCO (TEC2013-47665-C4-4-R) projects. The work of Das was partially supported by NSF Grants IIS-1404673, CNS-1355505, CNS-1404677 and DGE-1433659. Part of the work by Rodriguez-Carrion was conducted while she was visiting the Computer Science Department at Missouri University of Science and Technology in 2013-2014.