论文标题
在基于建议的信任系统中与不诚实意见作斗争的两级解决方案
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
论文作者
论文摘要
在本文中，我们提出了一种机制，以在收集和处理级别的基于建议的信任模型中处理不诚实意见。我们考虑了一个方案，在这种情况下，代理要求多方向其他代理人建立信任。在收集层面，我们建议允许代理商自我评估其建议的准确性，并自主决定他们是否会参与建议过程。在处理级别，我们提出了一种对勾结攻击弹性的建议聚合技术，然后为参与代理人提供了可信度更新机制。我们作品的独创性源于其在收集水平和处理水平上对不诚实意见的考虑，这可以更好地保护对不诚实推荐人。在Epinions数据集上进行的实验表明，与竞争模型相比，我们的解决方案在保护推荐过程免受SYBIL攻击方面的性能更好，该模型基于代理的信任值，该模型得出了最佳的顾问网络。
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in which an agent requests recommendations from multiple parties to build trust toward another agent. At the collection level, we propose to allow agents to self-assess the accuracy of their recommendations and autonomously decide on whether they would participate in the recommendation process or not. At the processing level, we propose a recommendations aggregation technique that is resilient to collusion attacks, followed by a credibility update mechanism for the participating agents. The originality of our work stems from its consideration of dishonest opinions at both the collection and processing levels, which allows for better and more persistent protection against dishonest recommenders. Experiments conducted on the Epinions dataset show that our solution yields better performance in protecting the recommendation process against Sybil attacks, in comparison with a competing model that derives the optimal network of advisors based on the agents' trust values.
