Publications - Pervasive Computing Laboratory

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2015
 Rodriguez-Carrion, Alicia;  Rebollo-Monedero, David;  Forne, Jordi;  Campo-Vázquez, Celeste;  García-Rubio, Carlos;  Parra-Arnau, Javier;  Das, Sajal
Entropy-based privacy against profiling of user mobility Journal Article 
In: Entropy, vol. 17, iss. 6, pp. 3913-3946, 2015, ISSN: 1099-4300.
Abstract | Links | BibTeX | Tags: emrisco, entropy, location history, location-based services, perturbative methods, privacy
@article{campo009,

title = {Entropy-based privacy against profiling of user mobility},

author = {Alicia Rodriguez-Carrion and David Rebollo-Monedero and Jordi Forne and Celeste Campo-Vázquez and Carlos García-Rubio and Javier Parra-Arnau and Sajal Das},

url = {http://hdl.handle.net/10016/27924},

doi = {https://doi.org/10.3390/e17063913},

issn = {1099-4300},

year  = {2015},

date = {2015-06-10},

urldate = {2015-06-10},

journal = {Entropy},

volume = {17},

issue = {6},

pages = {3913-3946},

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.},

keywords = {emrisco, entropy, location history, location-based services, perturbative methods, privacy},

pubstate = {published},

tppubtype = {article}

}

Close
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.
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http://hdl.handle.net/10016/27924
doi:https://doi.org/10.3390/e17063913
Close
2014
 Rodriguez-Carrion, Alicia;  Das, Sajal;  Campo-Vázquez, Celeste;  García-Rubio, Carlos
Impact of location history collection schemes on observed human mobility features Conference 
2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE - THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, INC , 2014, ISBN: 978-1-4799-2736-4.
Abstract | Links | BibTeX | Tags: data collection, location history, Mobile computing
@conference{nokey,

title = {Impact of location history collection schemes on observed human mobility features},

author = {Alicia Rodriguez-Carrion and Sajal Das and Celeste Campo-Vázquez and Carlos García-Rubio},

doi = {https://doi.org/10.1109/percomw.2014.6815213},

isbn = {978-1-4799-2736-4},

year  = {2014},

date = {2014-05-15},

urldate = {2014-05-15},

booktitle = {2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)},

pages = {254-259},

publisher = {IEEE - THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, INC },

abstract = {Human mobility knowledge is key for urban planning or mobility models design. Therefore, estimating reliable mobility parameters is crucial to lay an unbiased foundation. However, most works estimating such features rely on datasets made up of the history of mobile network cells where the user is located when she makes active use of the network, known as Call Data Records (CDRs), or every time the her device connects to a new cell, without taking into account cell changes not caused by movement. Could we accurately characterize human mobility with such datasets? In this work we consider three approaches to collect network-based mobility data, propose three filtering techniques to delete cell changes not caused by movement and compare mobility features extracted from the traces collected with each approach. The analysis unveils the need for a filtering step to avoid important biases, and the negative impact that using CDRs may have in estimating mobility parameters.},

keywords = {data collection, location history, Mobile computing},

pubstate = {published},

tppubtype = {conference}

}

Close
Human mobility knowledge is key for urban planning or mobility models design. Therefore, estimating reliable mobility parameters is crucial to lay an unbiased foundation. However, most works estimating such features rely on datasets made up of the history of mobile network cells where the user is located when she makes active use of the network, known as Call Data Records (CDRs), or every time the her device connects to a new cell, without taking into account cell changes not caused by movement. Could we accurately characterize human mobility with such datasets? In this work we consider three approaches to collect network-based mobility data, propose three filtering techniques to delete cell changes not caused by movement and compare mobility features extracted from the traces collected with each approach. The analysis unveils the need for a filtering step to avoid important biases, and the negative impact that using CDRs may have in estimating mobility parameters.
Close
doi:https://doi.org/10.1109/percomw.2014.6815213
Close