Abstract: With the popularization of various devices that provide location positioning services, while users enjoy the convenience brought by these devices, it also raises the issue of location privacy leakage. To address this problem, a trajectory data differential privacy protection scheme (SDTS) that combines contrast supervision and sorting tree is proposed. First, the supervised learning model is used to preprocess the trajectory data, and the loss function in the model is used to calculate the trajectory similarity and obtain the result. Second, a binary search tree structure is used to store the trajectory data, improv- ing the efficiency of trajectory queries. Finally, differential privacy technology and an equal privacy budget allocation method are used to add noise to the statistical values of moving users in the sorted tree nodes, protecting sensitive information stored in the nodes and ensuring data privacy security while improving data usability. Experimental results show that this scheme effectively protects users data privacy security and ensures the usability of trajectory data.

Key words: contrast loss function, differential privacy, binary sort tree, trajectory data, proportional allocation