Browsing by Subject "Trust management"

Now showing 1 - 2 of 2
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    LFTM, linguistic fuzzy trust mechanism for distributed networks
    (Wiley, 2011) Gómez Mármol, Félix; Marín Blázquez, Javier G.; Martínez Pérez, Gregorio; Ingeniería de la Información y las Comunicaciones
    Trust is, in some cases, being considered as a requirement in highly distributed communication scenarios. Before accessing a particular service, a trust model is then being used in these scenarios to determine if the service provider can be trusted or not. It is done usually on behalf of the final user or service customer, and with a little intervention of him or her. This is usually happening with the main aim of automatizing the process and because trust models are normally making use of reasoning mechanisms and models difficult to understand by humans. In this paper, we propose the adaptation of a bio-inspired trust model to deal with linguistic fuzzy labels, which are closer to the human way of thinking. This Linguistic Fuzzy Trust Model also uses fuzzy reasoning. Results show that the new model keeps the accuracy of the underlying bio-inspired trust model and the level of client satisfaction, while enhancing the interpretability of the model and thus making it closer to the final user.
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    Meta-Tacs: a trust model demonstration of robustness through a genetic algorithm
    (Tech Science Press, ) Gómez Marmol, Félix; Martínez Pérez, Gregorio; Marín Blázquez, Javier G.; Ingeniería de la Información y las Comunicaciones
    Ensuring trust and confidence in virtual communities ’ transactions is a critical issue nowadays. But even more important can become the use of robust and accurate trust models allowing an entity to decide which other entity to interact with. This paper aims to study the robustness of TACS (Trust Ant Colony System), a previously proposed bio-inspired P2P trust model, when applying a genetic algorithm in order to find the range of values of its working parameters that provides the best TACS performance. The optimization of those parameters has been carried out using the CHC genetic algorithm. Experiments seems to demonstrate that TACS can achieve high performance ratios due to the enhancement provided by META-TACS, and to achieve them for a wide range of working parameters, hence showing a remarkable robustness