Paper Info
Title
A feedback double filtering based model for evaluating reputation in Peer-to-Peer networks
Abstract
The feedback filtering is a key issue to the reputation evaluating in Peer-to-Peer networks. The existing models usually focus on filtering out the fake feedback at the trustor's side. However, even the most honest recommender could submit feedbacks with low quality. In this paper, a new reputation evaluating model is proposed. The model filters the feedback not only at the recommender's side but also at the trustor's side. Two new measures, i.e. confidence degree of feedback and trustworthiness degree of recommending, are introduced to the model. The former represents the confidence of the recommender to its feedback. This measure can be used by the recommender to filter out the feedback with low confidence. The latter is used to weight the recommender's feedback at the trustor's side. Experimental results show that our model has better performance and is robust even with large amount of malicious peers.
Year
DOI
Venue
2010
10.1109/ICMLC.2010.5581102
ICMLC
Keywords
Field
DocType
trustworthiness degree,reputation,trustworthiness degree of recommending,information filtering,peer-to-peer networks,feedback double filtering,reputation evaluating model,confidence degree of feedback,feedback filtering,peer-to-peer computing,peer to peer network,confidence degree,security of data,filtering,history,machine learning,reliability,cybernetics
Low Confidence,World Wide Web,Information retrieval,Peer-to-peer,Trustworthiness,Computer science,Peer to peer computing,Filter (signal processing),Artificial intelligence,Cybernetics,Machine learning,Reputation
Conference
Volume
ISBN
Citations 
1
978-1-4244-6526-2
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Yi-Ping Bao100.34
Li Yao2154.40
Wei Ming Zhang3696.72
Jiuyang Tang44612.86