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Entropy 2017, 19(7), 338; doi:10.3390/e19070338

Online Auction Fraud Detection in Privacy-Aware Reputation Systems

1
Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
2
Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 32003, Taiwan
3
Faculty of Management Science, Nakhon Ratchasima Rajabhat University, Nakhon Ratchasima 30000, Thailand
*
Author to whom correspondence should be addressed.
Received: 19 April 2017 / Revised: 20 June 2017 / Accepted: 2 July 2017 / Published: 5 July 2017
(This article belongs to the Section Information Theory)
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Abstract

With a privacy-aware reputation system, an auction website allows the buyer in a transaction to hide his/her identity from the public for privacy protection. However, fraudsters can also take advantage of this buyer-anonymized function to hide the connections between themselves and their accomplices. Traditional fraudster detection methods become useless for detecting such fraudsters because these methods rely on accessing these connections to work effectively. To resolve this problem, we introduce two attributes to quantify the buyer-anonymized activities associated with each user and use them to reinforce the traditional methods. Experimental results on a dataset crawled from an auction website show that the proposed attributes effectively enhance the prediction accuracy for detecting fraudsters, particularly when the proportion of the buyer-anonymized activities in the dataset is large. Because many auction websites have adopted privacy-aware reputation systems, the two proposed attributes should be incorporated into their fraudster detection schemes to combat these fraudulent activities. View Full-Text
Keywords: online auction; privacy; anonymity; fraudster detection online auction; privacy; anonymity; fraudster detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lin, J.-L.; Khomnotai, L. Online Auction Fraud Detection in Privacy-Aware Reputation Systems. Entropy 2017, 19, 338.

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