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Gil Pérez, Manuel

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Gil Pérez, Manuel
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Universidad de Murcia. Departamento de Ingeniería de la Informacióny las Comunicaciones
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    Precise: privacy-aware recommender based on context information for cloud service environments
    (Institute of Electrical and Electronics Engineers, 2014-08-31) Gil Pérez, Manuel; García Clemente, Félix Jesús; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
    Context-aware systems based on location open up new possibilities to users in terms of acquiring custom services by gathering context information, especially in systems where the high mobility of users increases their usability. In this context, this article presents a privacy-preserving solution offering context-aware services based on location in MCC. We propose a middleware, called PRECISE, which provides users with custom context-aware recommendations. These recommendations are given by considering the context information, and the users' locations, privacy policies, and previously visited places. MCC plays a key role in this solution, moving the data processing and storage needs to the cloud, as well as further advantages such as elasticity and load balancing. A thorough discussion when comparing PRECISE with other related works confirms that our solution improves the most relevant proposals so far.
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    SeCoMan: a semantic-aware policy framework for developing privacy-preserving and context-aware smart applications
    (Institute of Electrical and Electronics Engineers, 2014-01-21) García Clemente, Félix Jesús; Gil Pérez, Manuel; Martínez Pérez, Gregorio; Huertas Celdrán, Alberto; Ingeniería de la Información y las Comunicaciones; Facultad de Informática
    This paper is intended to provide a solution for developing context-aware smart applications preserving the users' privacy in the Internet of Things (IoT). In this sense, we present a framework called Semantic Web-based Context Management (SeCoMan) aimed at offering a set of predefined queries to provide applications with information about indoor location of users and objects, as well as context-aware services. SeCoMan uses a semantic-oriented IoT vision where semantic technologies play a key role. In fact, SeCoMan uses Semantic Web for modeling description of things, reasoning over data to infer new knowledge, and defining context-aware policies. SeCoMan also defines a layered architecture, including functions related to the management of the users' privacy in a manner that accommodate IoT requirements, in addition to not affecting system performance nor introducing excessive overheads. A thorough discussion on other related works, together with some experiments to measure the throughput and scalability, confirm that SeCoMan is a solution that improves the most relevant proposals existing so far.
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