Entropy-Based Privacy against Profiling of User Mobility

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 Entropy 2015, 17 3914 for tailoring the perturbative methods to the features of mobility profiles in order to improve privacy without completely loosing utility.

Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

“…Xiao et al [11] [19] studied how to protect location privacy under temporal correlations with an optimal differentially private mechanism. Rodriguez-Carrion et al [25] also studied the effect of temporal dependencies on entropybased location privacy metric. They proposed a new privacy metric entropy rate and perturbative mechanisms based on it, which can be an alternative LPPM in our framework for protecting spatiotemporal event privacy.…”

INRISCO: INcident monitoRing in Smart COmmunities

“…However, tracking user mobility rises privacy concerns. In [16] we analyzed privacy-enhancing mechanisms based on information theory concepts, such as entropy, applied to locations and mobility profiling scenarios. We have shown that the theory applicable to these low alphabet cardinality and memoryless processes, cannot be directly applied to more complex cases such as the mobility profiles of users.…”

PriSTE: From Location Privacy to Spatiotemporal Event Privacy

“…Xiao et al [9] [18] studied how to protect location privacy under temporal correlations with an optimal differentially private mechanism. Rodriguez-Carrion et al [23] also studied the effect of temporal dependencies on entropy-based location privacy metric. They proposed a new privacy metric entropy rate and perturbative mechanisms based on it, which can be an alternative LPPM in our framework for protecting spatiotemporal event privacy.…”