Using KL Divergence for Credibility Assessment
Résumé
In reputation systems, agents collectively estimate the oth-ers' behaviours through feedbacks to decide with whom they can interact. To avoid manipulations, most reputation systems weight feedbacks with respect to the agents' reputation. However, these systems are sensitive to some strategic manipulations, like oscillating attacks or whitewashing. In this paper, we propose (1) a credibility measure of feed-backs based on the Kullback-Leibler divergence to detect malicious behaviours and (2) filtering functions to enhance already known reputation functions.
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