An interoperable context sensitive model of trust

  • Authors:
  • Indrakshi Ray;Indrajit Ray;Sudip Chakraborty

  • Affiliations:
  • Department of Computer Science, Colorado State University, Fort Collins, USA 80523-1873;Department of Computer Science, Colorado State University, Fort Collins, USA 80523-1873;Department of Computer Science, Colorado State University, Fort Collins, USA 80523-1873

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Although the notion of trust is widely used in secure information systems, very few works attempt to formally define it or reason about it. Moreover, in most works, trust is defined as a binary concept--either an entity is completely trusted or not at all. Absolute trust on an entity requires one to have complete knowledge about the entity. This is rarely the case in real-world applications. Not trusting an entity, on the other hand, prohibits all communications with the entity rendering it useless. In short, treating trust as a binary concept is not acceptable in practice. Consequently, a model is needed that incorporates the notion of different degrees of trust. We propose a model that allows us to formalize trust relationships. The trust relationship between a truster and a trustee is associated with a context and depends on the experience, knowledge, and recommendation that the truster has with respect to the trustee in the given context. We show how our model can measure trust and compare two trust relationships in a given context. Sometimes enough information is not available about a given context to evaluate trust. Towards this end we show how the relationships between different contexts can be captured using a context graph. Formalizing the relationships between contexts allows us to extrapolate values from related contexts to approximate the trust of an entity even when all the information needed to calculate the trust is not available. Finally, we show how the semantic mismatch that arises because of different sources using different context graphs can be resolved and the trust of information obtained from these different sources compared.