To prevent the privacy disclosure caused by linking attack and reduce information loss resulting from anonymous protection, a (λα,k) multi-level anonymity model was proposed. According to the requirement of privacy preservation, sensitive attribute values could be divided into three levels: high, medium, and low. The risk of privacy disclosure was flexibly controlled by privacy protection degree parameter λ. On the basis of this, clustering-based approach for multi-level anonymization was proposed. The approach used a new hierarchical clustering algorithm and adopted more flexible strategies of data generalization for numerical attributes and classified attributes in a quasi-identifier. The experimental results show that the approach can meet the requirement of multi-level anonymous protection of sensitive attribute, and effectively reduce information loss.