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            <title xml:lang="fr">Préserver la confidentialité pour l'algorithme Generalized Distributed Breakout</title>
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                <forename type="first">Julien</forename>
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                <forename type="first">René</forename>
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              <p>Privacy has traditionally been a major motivation of distributed problem solving. In this paper, we focus on privacy issues when solving Distributed Constraint Optimization Problems (DCOPs). Recently, Okamoto, Zivan et Nahon promoted the Generalized Distributed Breakout Algorithm (GDBA) as a very efficient heuristic strategy to find good solutions to DCOPs. We study how GDBA behaves when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We show that GDBA can be improved to allow for extensive handling of constraint privacy, and reduce domain privacy loss by a factor 2~3 with no significant impact on the quality of the solution</p>
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              <p>Protéger la vie privée des utilisateurs est l’un des enjeux des systèmes distribués. Dans cet article, nous mesurons la fuite d’informations lors de la résolution de problèmes d’optimisation de contraintes distribués (DCOP). Récemment, Okamoto, Zivan et Nahon ont proposé l’algorithme de Breakout distribué généralisé (GDBA pour Generalized Distributed Breakout Algorithm) pour traiter ce type de problème. Cependant, la mesure et le contrôle des données échangées n’ont jamais été réalisés pour cet algorithme. Nous étudions le comportement de GDBA lors de la résolution de DCOP et de DCOP utilitaristes, une variante dans laquelle les agents cherchent une solution de qualité tout en protégeant les informations qu’ils possèdent. Nous montrons que GDBA peut être amélioré pour préserver la plupart des informations sur les contraintes et réduire la fuite de données sur les domaines d’un facteur 2 à 3 sans impact significatif sur la qualité des solutions</p>
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