| Abstract |
A traditional approach to reasoning about the trustworthiness of a transaction is to determine the trustworthiness of the specific agent involved, based on its past behavior. As a departure from such traditional trust models, we propose a transaction centered trust model (MetaTrust) where an agent uses its previous transactions to assess the trustworthiness of a potential transaction based on associated meta-information, which is capable of distinguishing successful transactions from unsuccessful ones. This meta information is harnessed using a machine learning algorithm (namely, discriminant analysis) to extract relationships between the potential transaction and previous transactions. |
