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Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs

We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices,...

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Detalles Bibliográficos
Autores principales: González-Tablas, Ana I., Tapiador, Juan E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883365/
https://www.ncbi.nlm.nih.gov/pubmed/27187385
http://dx.doi.org/10.3390/s16050674
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author González-Tablas, Ana I.
Tapiador, Juan E.
author_facet González-Tablas, Ana I.
Tapiador, Juan E.
author_sort González-Tablas, Ana I.
collection PubMed
description We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user’s security preferences implicitly captured by policies already in place.
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spelling pubmed-48833652016-05-27 Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs González-Tablas, Ana I. Tapiador, Juan E. Sensors (Basel) Article We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user’s security preferences implicitly captured by policies already in place. MDPI 2016-05-11 /pmc/articles/PMC4883365/ /pubmed/27187385 http://dx.doi.org/10.3390/s16050674 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
González-Tablas, Ana I.
Tapiador, Juan E.
Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs
title Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs
title_full Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs
title_fullStr Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs
title_full_unstemmed Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs
title_short Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs
title_sort bootstrapping security policies for wearable apps using attributed structural graphs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883365/
https://www.ncbi.nlm.nih.gov/pubmed/27187385
http://dx.doi.org/10.3390/s16050674
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