Local differential privacy （LDP） is a well-regarded privacy protection model widely used in data collection and statistical analysis to address privacy concerns. However， LDP does not account for the varying privacy needs of different users and the differences in data attributes. As a variant of LDP， the personalized local differential privacy （PLDP） has been proposed. This paper categorizes PLDP mechanisms into two types based on the aforementioned differences and analyzes the current research status in this field. Firstly， the paper introduces the basic concepts and theoretical models of PLDP. Then， it analyzes and classifies several recent studies on PLDP mechanisms， providing detailed explanations of representative schemes， including data perturbation methods and data aggregation methods. Finally， the paper discusses and analyzes the future development directions in this field.