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Fernández Valdivielso, Carlos

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https://academica-e.unavarra.es/handle/2454/49023

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Fernández Valdivielso

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Carlos

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Ingeniería Eléctrica, Electrónica y de Comunicación

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0009-0005-3397-5861

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2704

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2006 - 20103
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Author: Alcalde Bagüés, Susana ×

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Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen Access
    Towards personal privacy control
    (Springer, 2007) Alcalde Bagüés, Susana; Zeidler, Andreas; Fernández Valdivielso, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
    In this paper we address the realization of personal privacy control in the era of pervasive computing. How could an individual meet his/her expected level of privacy? how could the system guaranty that a user privacy criteria is fulfilled?. For that an elaborate set of requirements for personal privacy is given followed with the implementation of our SenTry policy language.
  • Thumbnail Image
    PublicationOpen Access
    Enabling personal privacy for pervasive computing environments
    (Graz University of Technology, IICM, 2010) Alcalde Bagüés, Susana; Zeidler, Andreas; Klein, Cornel; Fernández Valdivielso, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
    Protection of personal data in the Internet is already a challenge today. Users have to actively look up privacy policies of websites and decide whether they can live with the terms of use. Once discovered, they are forced to make a ”‘take or leave”’ decision. In future living and working environments, where sensors and context-aware services are pervasive, this becomes an even greater challenge and annoyance. The environment is much more personalized and users cannot just ”‘leave”’. They require measures to prevent, avoid and detect misuse of sensitive data, as well as to be able to negotiate the purpose of use of data. We present a novel model of privacy protection, complementing the notion of enterprise privacy with the incorporation of personal privacy towards a holistic privacy management system. Our approach allows non-expert users not only to negotiate the desired level of privacy in a rather automated and simple way, but also to track and monitor the whole life-cycle of data.
  • Thumbnail Image
    PublicationOpen Access
    A user-centric privacy framework for pervasive environments
    (Springer, 2006) Alcalde Bagüés, Susana; Zeidler, Andreas; Fernández Valdivielso, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
    One inherent feature of pervasive computing environments is the need to gather and process context information about real persons. Unfortunately, this unavoidably affects persons¿ privacy to a large degree. Each time today a citizen uses his cellular phone, his credit card or surf the web, he is leaving a trace that is stored for some reason. In a pervasive sensing environment, however, the amount of information collected is a) much larger than today and b) might be used to reconstruct personal information with great accuracy. The question we address in this paper is to control dissemination and flow of personal data across organizational, as well as personal boundaries, i.e., to potential addressees of privacy relevant information. This paper presents the User-Centric Privacy Framework (UCPF). It aims at protecting a user¿s privacy based on the enforcement of privacy preferences. They are expressed as a set of constraints over some set of context information. To achieve the goal of cross-boundary control, we introduce two novel abstractions, namely Transformations and Foreign Constraints, in order to extend the possibilities of a user to describe his privacy protection criteria beyond the current expressiveness ussually found today. Transformations are understood as any process that the user may define over a specific piece of context. This is a main building block for obfuscating or even plainly lie about the context in question. Foreign Constraints are an important complementing extension because they allow for modeling conditions defined on external users that are not the tracked individual, but may influence disclosure of personal data to third parties. We are confident that these two easy-to-use abstractions together with the general privacy framework presented in this paper constitute a strong contribution to the protection of the personal privacy in pervasive computing environments.