Diaz-Sánchez, Daniel; Sherratt, Simon; Arias, Patricia; Almenarez, Florina; Marín, Andrés Enabling actor model for crowd sensing and IoT Proceedings Article In: IEEE, 2015, ISSN: 0747-668X. Abstract | Links | BibTeX | Tags: Cloud computing, emrisco, IoT, Wireless communication Rodriguez-Carrion, Alicia; García-Rubio, Carlos; Campo-Vázquez, Celeste; Das, Sajal Analysis of a fast LZ-based entropy estimator for mobility data Conference 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), IEEE Publishing Services , 2015, ISBN: 978-1-4799-8425-1. Abstract | Links | BibTeX | Tags: emrisco, entropy, lz, mobility data 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 Arias-Cabarcos, Patricia; Almenárez, Florina; Trapero, Rubén; Díaz-Sánchez, Daniel; Marín, Andrés Blended Identity: Pervasive IdM for Continuous Authentication Journal Article In: IEEE Xplore, vol. 13, iss. 3, pp. 32-39, 2015, ISSN: 1540-7993. Abstract | Links | BibTeX | Tags: blended identity, emrisco, identity management, IdM, Pervasive computing, Protocols, risk assessment, Security Díaz-Sanchez, Daniel; Arias-Cabarcos, Patricia; Almenarez, Florina; Marín-López, Andrés P2P-based data layer for mobile Media Cloud Proceedings Article In: IEEE, 2015, ISSN: 2158-3994. Abstract | Links | BibTeX | Tags: Cloud computing, emrisco, Protocols2015
@inproceedings{pa006,
title = {Enabling actor model for crowd sensing and IoT},
author = {Daniel Diaz-Sánchez and Simon Sherratt and Patricia Arias and Florina Almenarez and Andrés Marín},
url = {https://ieeexplore.ieee.org/document/7177779},
doi = {https://doi.org/10.1109/ISCE.2015.7177779},
issn = {0747-668X},
year = {2015},
date = {2015-08-06},
urldate = {2015-08-06},
publisher = {IEEE},
abstract = {The cloud is playing a very important role in wireless sensor network, crowd sensing and IoT data collection and processing. However, current cloud solutions lack of some features that hamper the innovation a number of other new services. We propose a cloud solution that provides these missing features as multi-cloud and device multi-tenancy relying in a whole different fully distributed paradigm, the actor model.},
keywords = {Cloud computing, emrisco, IoT, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
@conference{campo017b,
title = {Analysis of a fast LZ-based entropy estimator for mobility data},
author = { Alicia Rodriguez-Carrion and Carlos García-Rubio and Celeste Campo-Vázquez and Sajal Das },
doi = {https://doi.org/10.1109/percomw.2015.7134080},
isbn = {978-1-4799-8425-1},
year = {2015},
date = {2015-06-29},
urldate = {2015-06-29},
booktitle = {2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)},
pages = {451-456},
publisher = {IEEE Publishing Services },
abstract = {Randomness in people's movements might serve to detect behavior anomalies. The concept of entropy can be used for this purpose, but its estimation is computational intensive, particularly when processing long movement histories. Moreover, disclosing such histories to third parties may violate user privacy. With a goal to keep the mobility data in the mobile device itself yet being able to measure randomness, we propose three fast entropy estimators based on Lempel-Ziv (LZ) prediction algorithms. We evaluated them with 95 movement histories of real users tracked during 9 months using GSM-based mobility data. The results show that the entropy tendencies of the approaches proposed in this work and those in the literature are the same as time evolves. Therefore, our proposed approach could potentially detect variations in the mobility patterns of the user with a lower computational cost. This allows to unveil shifts in the users mobility behavior without disclosing their sensible location data.},
keywords = {emrisco, entropy, lz, mobility data},
pubstate = {published},
tppubtype = {conference}
}
@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}
}
@article{ariascabarcos002,
title = {Blended Identity: Pervasive IdM for Continuous Authentication},
author = {Patricia Arias-Cabarcos and Florina Almenárez and Rubén Trapero and Daniel Díaz-Sánchez and Andrés Marín},
url = {https://ieeexplore.ieee.org/document/7118079},
doi = {https://doi.org/10.1109/MSP.2015.62},
issn = {1540-7993},
year = {2015},
date = {2015-06-04},
urldate = {2015-06-04},
journal = {IEEE Xplore},
volume = {13},
issue = {3},
pages = {32-39},
abstract = {A proper identity management approach is necessary for pervasive computing to be invisible to users. Federated identity management is key to achieving efficient identity blending and natural integration in the physical and online layers where users, devices, and services are present.},
keywords = {blended identity, emrisco, identity management, IdM, Pervasive computing, Protocols, risk assessment, Security},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{pa005,
title = {P2P-based data layer for mobile Media Cloud},
author = {Daniel Díaz-Sanchez and Patricia Arias-Cabarcos and Florina Almenarez and Andrés Marín-López},
url = {https://ieeexplore.ieee.org/document/7066362},
doi = {https://doi.org/10.1109/ICCE.2015.7066362},
issn = {2158-3994},
year = {2015},
date = {2015-03-26},
urldate = {2015-03-26},
publisher = {IEEE},
abstract = {This paper focus in an emerging concept called Elastic Personal Computing that is the ability to distribute data processing among multiple personal devices that constitute a mobile cloud. Among the most complex challenges is to provide data layer for the system to exchange input data transparently among nodes considering the data partitioning is application specific. Implementing data layers with replication and load distribution strategies is not feasible due to mobility, intermittent availability and the distributed character of mobile cloud systems. This article reasons about the problem and presents a P2P based data layer for distributed computing using personal devices.},
keywords = {Cloud computing, emrisco, Protocols},
pubstate = {published},
tppubtype = {inproceedings}
}
Publications
Enabling actor model for crowd sensing and IoT Proceedings Article In: IEEE, 2015, ISSN: 0747-668X. Analysis of a fast LZ-based entropy estimator for mobility data Conference 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), IEEE Publishing Services , 2015, ISBN: 978-1-4799-8425-1. Entropy-based privacy against profiling of user mobility Journal Article In: Entropy, vol. 17, iss. 6, pp. 3913-3946, 2015, ISSN: 1099-4300. Blended Identity: Pervasive IdM for Continuous Authentication Journal Article In: IEEE Xplore, vol. 13, iss. 3, pp. 32-39, 2015, ISSN: 1540-7993. P2P-based data layer for mobile Media Cloud Proceedings Article In: IEEE, 2015, ISSN: 2158-3994.2015