Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

Rodríguez-Carrión, Alicia; Garcia-Rubio, Carlos; Campo, Celeste; Cortés-Martín, Alberto; Garcia-Lozano, Estrella; Noriega-Vivas, Patricia

doi:10.3390/s120607496

Cited by 24 publications

(15 citation statements)

References 29 publications

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“…The adversary's goal is then to be able to predict as accurately as possible the next location of the user, given her past mobility history. There exists many prediction algorithms that can be used to do so [56], and their success depends on the predictability of the mobility history. As demonstrated in [3], the temporal dependencies among the locations visited by the user enclose information that noticeably increases the predictability of the mobility.…”

Section: Mobility Profilementioning

confidence: 99%

Entropy-Based Privacy against Profiling of User Mobility

Rodríguez-Carrión

Rebollo-Monedero

Forné

et al. 2015

Entropy

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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.

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Section: Mobility Profilementioning

confidence: 99%

Entropy-Based Privacy against Profiling of User Mobility

Rodríguez-Carrión

Rebollo-Monedero

Forné

et al. 2015

Entropy

Self Cite

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“…For example, the work in [2] proposes a proactive contextualization by means of prediction. A lightweight application that resides in the user's phone constantly learns her mobility pattern according to the GSM cells to which it registers.…”

Section: Related Workmentioning

confidence: 99%

A Bandwidth-Efficient Service for Local Information Dissemination in Sparse to Dense Roadways

Garcia-Lozano

Campo

Garcia-Rubio

et al. 2013

Sensors

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Thanks to the research on Vehicular Ad Hoc Networks (VANETs), we will be able to deploy applications on roadways that will contribute to energy efficiency through a better planning of long trips. With this goal in mind, we have designed a gas/charging station advertising system, which takes advantage of the broadcast nature of the network. We have found that reducing the number of total sent packets is important, as it allows for a better use of the available bandwidth. We have designed improvements for a distance-based flooding scheme, so that it can support the advertising application with good results in sparse to dense roadway scenarios.

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“…Rodriguez-Carrion [25,26] assessed three LZ-based algorithms by separating each algorithm into two independent phases (tree building and probability calculation) and further discussing hit rate and power consumption. Rodriguez-Carrion [25,26] assessed three LZ-based algorithms by separating each algorithm into two independent phases (tree building and probability calculation) and further discussing hit rate and power consumption.…”

Section: Introductionmentioning

confidence: 99%

User location prediction with energy efficiency model in the Long Term‐Evolution network

Qiao

Yang

et al. 2015

Int J Communication

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Predicting users' next location/place allows us to anticipate their future movement. It provides additional time to be ready for that movement and react consequently. Furthermore, many industries, including Internet Service Providers, are still requiring low cost and simple location/place prediction methods that can be implemented on mobile device. This paper studies domain-independent prediction algorithms and spatiotemporal based prediction method using 20-day-long records in Long Term-Evolution(LTE) network, which captures the mobility patterns of 3474 individuals. After examining the prediction accuracy and resource consumption of domain-independent prediction algorithms, we find Markov provides the best tradeoff. Furthermore, Active LeZi outperforms Markov if enough consecutive parsed patterns of users' history movement are captured. In addition, we further group users according to their spatio-temporal entropy profiles in order to predict not only user's future locations but also the place he or she most likely to appear within a specific period. By applying the simple spatio-temporal based method to each group of user, 83.3% accuracy can be achieved for some users. Yet Markov and Active LeZi algorithms perform better for some other users. This implies that we should consider applying different prediction methods to users with distinct spatio-temporal characteristics. RELATED WORKIndividuals display significant regularity, as they tend to visit a few highly frequented locations, like home or office. Numerous studies show that people's movement trajectory is far from random. By measuring the entropy of each individual's trajectory, [1] achieved 93% potential predictability in user mobility. When considering both the frequencies and temporal correlations of individual movements, the theoretical maximum predictability can reach 88% [2].Previous studies regarding location prediction include several models and methods. The usage of well-known mobility models was originally applied in the area of location predication [3,4], like Bayesian approaches [5-7], neural networks [8], Hidden Markov models [9], Markov models [10] and compression algorithms [11-13]. In addition, some recently proposed new algorithms [14, 15] and frames [16-18] all presented very good results.Recently, regarding the location prediction, scholars start to consider many other factors that could influence the prediction results, like spatial context [4], temporal factors [19,20], spatio-temporal factors [21,22] and even demographics (such as gender and age) [23].

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Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

Cited by 24 publications

References 29 publications

Entropy-Based Privacy against Profiling of User Mobility

Entropy-Based Privacy against Profiling of User Mobility

A Bandwidth-Efficient Service for Local Information Dissemination in Sparse to Dense Roadways

User location prediction with energy efficiency model in the Long Term‐Evolution network

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