Fifth International Conference on Information Technology: New Generations (Itng 2008) 2008
DOI: 10.1109/itng.2008.22
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A Framework for Agent-Based Trust Management in Online Auctions

Abstract: Abstract

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Cited by 26 publications
(27 citation statements)
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References 12 publications
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“…As part of our previous work, Xu et al presented a multiagent trust management (ATM) framework for online auctions [2]. The shill inference procedure discussed in this paper can be embedded in the security agent of ATM.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…As part of our previous work, Xu et al presented a multiagent trust management (ATM) framework for online auctions [2]. The shill inference procedure discussed in this paper can be embedded in the security agent of ATM.…”
Section: Related Workmentioning
confidence: 99%
“…Our approach can be abstracted as a 5-tuple <B, δ, P, θ, φ>, where 2 ,…,b n } is a set of online auction bidders; 2. δ: B [0, 1] is a scoring function. Every online auction bidder has a shilling score, and δ(b i ) is defined as the shilling score for bidder i; 3.…”
Section: An Abstract Modelmentioning
confidence: 99%
“…Trust management in online auction systems Xu (2008) presented a Multi-Agent Trust Management (ATM) framework for online auctions. The shill inference procedure was embedded in the security agent of ATM.…”
Section: Related Workmentioning
confidence: 99%
“…The shill inference procedure was embedded in the security agent of ATM. (Xu, 2008) introduced a formal model checking approach to detect shilling behaviors, especially the competitive shilling behaviors (Cheng, 2007). Wood statistically analyzed data from rare coin auctions on eBay, and empirically tested the questionable bidding behaviors that are attributable to shill bidding (Wood, 2003).…”
Section: Related Workmentioning
confidence: 99%
“…A new framework for agent based trust management in online auction was proposed in 2008 in the paper [13] to facilitate real time trust re-evaluation by updating user roles and access permissions dynamically, where the reputation models depend on prior history of users and/or feedback from the other entities, and there is no effective mechanism to prevent users from giving false information when making a recommendation.…”
Section: Predicting the Participants' Behavior Using Feedback Featuresmentioning
confidence: 99%