Publications

@article{campo003,

title = {Entropy-Based Anomaly Detection in HouseholdElectricity Consumption},

author = {Marta Moure-Garrido and Celeste Campo-Vázquez and Carlos García-Rubio},

doi = {https://doi.org/10.3390/en15051837},

issn = {1996-1073},

year  = {2022},

date = {2022-03-02},

urldate = {2022-03-02},

journal = {Energies},

volume = {15},

abstract = {Energy efficiency is one of the most important current challenges, and its impact at a global level is considerable. To solve current challenges, it is critical that consumers are able to control their energy consumption. In this paper, we propose using a time series of window-based entropy to detect anomalies in the electricity consumption of a household when the pattern of consumption behavior exhibits a change. We compare the accuracy of this approach with two machine learning approaches, random forest and neural networks, and with a statistical approach, the ARIMA model. We study whether these approaches detect the same anomalous periods. These different techniques have been evaluated using a real dataset obtained from different households with different consumption profiles from the Madrid Region. The entropy-based algorithm detects more days classified as anomalous according to context information compared to the other algorithms. This approach has the advantages that it does not require a training period and that it adapts dynamically to changes, except in vacation periods when consumption drops drastically and requires some time for adapting to the new situation.},

keywords = {anomaly detection, behavior pattern, compromise, cynamon, entropy, household electricity consumption, load forecasting, magos},

pubstate = {published},

tppubtype = {article}

}

@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}

}