Towards reliable trust establishment in grid: a pre-evaluating set based bias-tuned method for dishonest feedback filtering

Reputation-based trust system emerges as a promising mechanism for trust establishment between unknown entities in Grid, in which the reliability of first-hand ratings plays a crucial part. In this paper, we propose a pre-evaluating set based bias-tuned approach for dishonest feedback filtering in such system, which has taken reputation's subjective feature and Grid entity's prevailing strangeness into consideration. The basic idea for filtering is to find inconsistency between a rater's ratings and his usual rating habit. The introduction of the pre-evaluating set is to provide a way for rating habit tracking. The proposed filtering method consists of two parts: "credibility filtering" and "on-spot filtering". The former tries to find inconsistency in ratings given to entities familiar to the current evaluator. And the latter tries to find inconsistency in the current retrieved rating. In combination of the two parts, we can effectively filter out dishonest feedbacks and retain honest ones to a large extent.