RLM: A General Model for Trust Representation and Aggregation

Reputation-based trust systems provide important capability in open and service-oriented computing environments. Most existing trust models fail to assess the variance of a reputation prediction. Moreover, the summation method, widely used for reputation feedback aggregation, is vulnerable to malicious feedbacks. This paper presents a general trust model, called RLM, for a more comprehensive and robust reputation evaluation. Concretely, the authors define a comprehensive reputation evaluation method based on two attributes: reputation value and reputation prediction variance. The reputation predication variance serves as a quality measure of the reputation value computed based on aggregation of feedbacks. For feedback aggregation, they propose the novel Kalman aggregation method, which can inherently support robust trust evaluation.

Provided by: Institute of Electrical & Electronic Engineers Topic: Security Date Added: Oct 2010 Format: PDF

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