Reputation-Based Multi-Dimensional Trust Algorithm

Abstract

For the mobile-agent-based e-commerce environment, most reputation-based trust algorithms are onedimensional. They are only based on the node's historical transactions, and the services are not taken into account, so the evaluations are coarse-gained. This paper proposes a reputation-based multi-dimensional trust (RMDT) algorithm which makes use of a self-confident coefficient to synthesize the directed and the reference trustworthiness to evaluate the node in the network. The time sensitive function in this paper not only serves as a reward and punishment mechanism, but is also dynamically attenuate in its trustworthiness. Both the transaction evaluation system and the weight system are introduced in the multi-dimensional trust mechanism. RMDT can uncover the influence on trust computation caused by the subjective factors, such as individual predilection and risk attitude. In addition, the sensitivity of RMDT on the single attribute is greatly improved.

Dynamic trust evaluation model based on bidding and multi-attributes for social networks

“…Sabater and Sierra (2001) proposed a trust system called REGRET which computes the final trust value of nodes by integrating varied reputation with a graded ontology structure and social networks analysis. Gan et al (2011) proposed a multi-dimensionalities reputation computing method in electronic commerce by dividing trust into four dimensions and building utility function as one of the computing direct trust weight. Meanwhile, distinguishing recommendation trustworthiness and scale of recommendation nodes by relationship between recommendation nodes improves accuracy and objectivity of trust computing.…”

“…Direct trust computing involved with service successes, volume of service, etc. (Gan et al, 2011). However, the past computing method of indexes weigh existed defect of subjective assumption and the method is both trivial and uncertain.…”

A game theory-based trust measurement model for social networks

“…The model considers the inherent connection among trust, reputation and incentive and the effect of time factor on the trust computation. Gan et al [ 24 ] proposed a reputation-based multi-dimensional trust (RMDT) algorithm which makes use of a self-confident coefficient to synthesize the direct and recommendation trust to evaluate the nodes in a network. A multi-dimensional trust mechanism is also introduced to improve sensitivity of RMDT on a single attribute.…”

A Novel Iterative and Dynamic Trust Computing Model for Large Scaled P2P Networks

“…This method has higher detection rate of malicious nodes, but it can only avoid simple malicious behaviors and cannot defend against various attacks on trust mechanism. Gan et al [20] present a new method for trust recommendation. This method uses confidence factor to comprehend direct and recommendation trust and establishes reward and punishment model to encourage fair global recommendation.…”