RT Journal Article
T1 Entropy-based privacy against profiling of user mobility
A1 Rodríguez Carrión, Alicia
A1 Rebollo Monedero, David
A1 Forne, Jordi
A1 Campo Vázquez, María Celeste
A1 García Rubio, Carlos
A1 Parra Arnau, Javier
A1 Das, Sajal K.
AB 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.
PB MDPI
SN 1099-4300
YR 2015
FD 2015-06-10
LK https://hdl.handle.net/10016/27924
UL https://hdl.handle.net/10016/27924
LA eng
NO 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.
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RD 1 sept. 2024