Efficient and Correct Trust Propagation Using CloseLook

Computational trust propagation is an important method for the establishment of trust in strangers. In ad-hoc or P2P networks, such an approach allows to choose trusted nodes for routing, data storage, or computation, even if the choosing node has not had previous experiences with the considered nodes. Human trust propagation can occur through a variety of phenomena, such as recommendation of trusted strangers (transitive trust propagation) or because of similarity between the trustor and trustee (similarity propagation). Computational trust propagation algorithms aim to reproduce the process of human trust propagation faithfully and to exploit all available information in order to recommend new trust links. Research in this area has established a method of evaluating the correctness of trust propagation algorithms that takes into account the recall of trust recommendation. In this paper, this commonly used evaluation method is criticized, and a new method is proposed that additionally allows to approximate the precision of trust recommendations. The second contribution of the paper is CloseLook, a new trust propagation algorithm that is capable of executing all relevant types of trust propagation in an efficient manner. The efficiency of CloseLook is compared against a well known trust propagation algorithm proposed by Guha et al. CloseLook is much more efficient without sacrificing correctness.