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.