Comparing Local, Collective, and Global Trust Models - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue International Journal On Advances in Life Sciences Année : 2014

Comparing Local, Collective, and Global Trust Models

Résumé

—Today, trust modelling is a serious issue on the social web. Social web allows information exchange between anonymous users who have no prior knowledge to each other. The aim of a trust model is to rerank acquired information according to their reliability and the trustworthiness of their author. During the last decade, trust models were proposed to assist the user to state his opinion about the acquired information, and about their sources. We identify three paradigms for trust modelling: the first relies on evaluating previous interactions with the source (individual trust), the second relies on the word of mouth paradigm where the user relies on the knowledge of his friends and their friends (collective trust), and the third relies on the reputation of the source (global trust). In this paper, we propose and compare three trust models, each of which represent one of the precedent paradigms. All three models make use of subjective logic (SL). SL is an extension of probabilistic logic that deals with the cases of lack of evidence. It supplies framework for modelling trust on the web. The comparison includes three axes: the precision, the complexity and the robustness to malicious attacks. We show that each of the three models has a weak point in one of the three axes.
Fichier principal
Vignette du fichier
iara-charif_j1.pdf (281.62 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01109270 , version 1 (25-01-2015)

Identifiants

  • HAL Id : hal-01109270 , version 1

Citer

Charif Haydar, Azim Roussanaly, Anne Boyer. Comparing Local, Collective, and Global Trust Models. International Journal On Advances in Life Sciences, 2014, 6 (1&2), pp.10. ⟨hal-01109270⟩
176 Consultations
89 Téléchargements

Partager

Gmail Facebook X LinkedIn More