Trust Modeling with Context Representation and Generalized Identities

Abstract

We present a trust model extension that attempts to relax the assumptions that are currently taken by the majority of existing trust models: (i) proven identity of agents, (ii) repetitive interactions and (iii) similar trusting situations. The proposed approach formalizes the situation (context) and/or trusted agent identity in a multi-dimensional Identity-Context feature space, and attaches the trustworthiness evaluations to individual elements from this metric space, rather than to fixed identity tags (e.g. AIDs, addresses). Trustworthiness of the individual elements of the I-C space can be evaluated using any trust model that supports weighted aggregations and updates, allowing the integration of the mechanism with most existing work. Trust models with the proposed extension are appropriate for deployment in dynamic, ad-hoc and mobile environments, where the agent platform can’t guarantee the identity of the agents and where the cryptography-based identity management techniques may be too costly due to the unreliable and costly communication.