ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (12): 2685-2702.doi: 10.7544/issn1000-1239.2018.20170587

• 人工智能 • 上一篇    下一篇



  1. 1(山东科技大学计算机科学与工程学院 山东青岛 266590);2(山东省智慧矿山信息技术重点实验室(山东科技大学) 山东青岛 266590) (
  • 出版日期: 2018-12-01
  • 基金资助: 

A Trust-Distrust Based Reputation Attacks Defending Strategy and Its Stability Analysis

Ma Haiyan1, Liang Yongquan2, Ji Shujuan2, Li Da1   

  1. 1(College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590); 2(Shandong Provincial Key Laboratory of Wisdom Mine Information Technology (Shandong University of Science and Technology), Qingdao, Shandong 266590)
  • Online: 2018-12-01

摘要: 虽然各电子商务企业采用多种信誉评价机制解决消费者对卖方或平台的信任问题,但是信誉评价系统仍然频繁地遭受各种攻击.这些攻击致使卖方的信誉排名和推荐排名被操控,大量诚实消费者被误导而购买了质量较差的商品.有研究者提出综合考虑消费者之间的信任和不信任信息可以使消费者更好地抵御信誉攻击.然而,现有工作存在“信任或不信任信息融合不足”以及“使用一组顾问评价所有卖方”等局限性,因此导致卖方信誉评价的针对性和准确性较差.提出了一种新的防御策略——T&D,它综合考虑了消费者的可信和不可信2个方面.此外,该策略为消费者设置了白名单(存储若干个最信任的评价者)和黑名单(存储若干个最不信任的评价者).利用黑名单净化白名单,诚实消费者可以找到更可信的评价者并依据这些评价者的评分和诚实消费者自身的经历准确评估每个卖方的信誉值.模拟实验结果显示:该策略在评估准确性和稳定性方面明显优于现有防御策略.

关键词: 信誉系统, 攻击, 防御策略, 信任, 黑名单, 白名单

Abstract: Though electronic commerce companies adopt multifarious reputation evaluation mechanisms to guarantee trust between customers and sellers (or customers and platforms), these reputation evaluation systems are still frequently attacked. These attacks have led the reputation ranking and recommendation rankings of sellers to be manipulated. Therefore, large numbers of honest consumers are misled to purchase low quality products. It has been mentioned that overall consideration of trust and distrust information can improve customers’ ability in defensing against reputation attacks. However, existing works have limitations such as “the trust information and distrust information are less fused”, “one advisor list is used in evaluating all sellers”, which leads to the lack of pertinence and inaccuracy of sellers’ reputation evaluation. We propose a new defensing strategy called T&D. This strategy considers the trustworthy facet as well as the untrustworthy facet of customers. In addition, this strategy offers a whitelist (which stores several most trustworthy reviewers) and a blacklist (which stores several most untrustworthy reviewers) for customers. Based on the whitelist that is purified using the blacklist, honest customers can find the most trustworthy buyers and evaluate the candidate sellers according to its own experience and ratings of these trustworthy reviewers. Simulated experimental results show that our proposed strategy significantly outperforms state-of-the-art baselines in evaluation accuracy and stability.

Key words: reputation system, attack, defense strategy, trust, blacklist, whitelist