Defending online reputation systems against collaborative unfair raters through signal modeling and trust
Defending online reputation systems against collaborative unfair raters through signal modeling and trust
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DOI：
10.1145/1529282.1529575
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发表时间：
2009-03
期刊：
影响因子：
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通讯作者：
Yafei Yang;Y. Sun;S. Kay;Qing Yang
中科院分区：
文献类型：
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作者：
Yafei Yang;Y. Sun;S. Kay;Qing Yang
基于在线反馈的评级系统越来越受欢迎。在这样的系统中处理协作不公平评级一直被认为是一个重要但困难的问题。这个问题具有挑战性，特别是当诚实评级的数量相对较少，而不公平评级可能占整体评级的很大一部分的时候。此外，缺乏来自真实人类用户的不公平评级数据是对防御机制进行现实评估的另一个障碍。在本文中，我们提出了一套基于信号建模的联合检测智能和协作不公平评级的方法。在此基础上，提出了一个信任度辅助评级聚合系统的框架。此外，我们设计并发起了一个评级挑战，以收集真实人类用户的不公平评级数据。使用真实攻击数据通过仿真和实验对所提出的系统进行了评估。与现有方案相比，该系统可以显著降低协同不公平评分带来的影响。
Online feedback-based rating systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of honest ratings is relatively small and unfair ratings can contribute to a significant portion of the overall ratings. In addition, the lack of unfair rating data from real human users is another obstacle toward realistic evaluation of defense mechanisms. In this paper, we propose a set of methods that jointly detect smart and collaborative unfair ratings based on signal modeling. Based on the detection, a framework of trust-assisted rating aggregation system is developed. Furthermore, we design and launch a Rating Challenge to collect unfair rating data from real human users. The proposed system is evaluated through simulations as well as experiments using real attack data. Compared with existing schemes, the proposed system can significantly reduce the impact from collaborative unfair ratings.