Trust-decisions on the of maximal information of recommended direct-trust

Abstract

Nowadays the concept of trust in computer communications starts to get more and more popular. While the idea of trust in human interaction seems to be obvious and understandable it is very difficult to find adequate and precise definitions of the trust-term. Even more difficult is the attempt to find computable models of trust, particularly if one tries to keep all psycho-sociological morality from the real life out of the model. But, apart from all these problems, some approaches have been introduced more or less successful. In this paper, we do not create a new definition of trust. Like many others we start with the simplest definition of trust as a probability of expected positive behaviour. Our focus lies on the question, how far recommended trust-information is suitable to be the base of a trust-decision. Our concept is based on the definition, that individual experiences are essential for a directional direct-trust relation between an entity and an opposite entity. Recommendation-trust is a special direct-trust-relation. In order to be able to make trust-decisions on the base of recommended trust-information, our solution does not try to condense the chains of recommendation to only one value, but keeps the information untouched. We introduce trust-decisions as the final step of a randomly chosen path in a decision-tree where the weighted edges of the tree consist of recommended trust-values or the new introduced certainty-values, and of leafs with direct-trust-values or sections of total inexperience. A trust-decision is positive when a trust-threshold is exceeded by the determined value of the process. The calculation of the new introduced certainty-values, indicating the probability of the procedure to reach a direct-trust-value inside a sub-tree, plays a big part in this approach. One advantage of the procedure to induce the trust-decisions on the base of randomness lies in the higher resistance against false information from malicious entities because with a probability, paths through the tree will be chosen which exclude information of these entities. Besides the new approach of trust-decisions on the base of recommended trust-information, we show how far (meaning with how many recommenders) it is reasonable to recommend trust-information. We will give suggestions how to optimize the tree of recommendation, certainty, and direct-trust, so that in an adequate time trust-decisions are possible and we show the influence of bad and malicious entities on the results of the trust-decision. Copyright 2006 ACM.