UNIRE: Secure Trajectory-User Linking Model Aggregation with Knowledge Transfer

Machine learning-based trajectory-user linking (TUL) task, which explores human mobility patterns and identifies known users from anonymous trajectories, is widely applied in location-based personalized recommendation systems. However, TUL models implicitly and inadvertently retain users historical trajectories in training data, which could be revealed under malicious inference and analysis. To address this issue, we propose UNIRE, a knowledge transfer-style TUL model aggregation framework to protect sensitive training data. Specifically, teacher TUL models are trained with private users trajectories and then utilized to collaboratively annotate a sensitive-trajectories-isolated dataset. Leveraging this teacher-labeled dataset, student TUL model can be trained and released as the secure aggregation of teacher models, preventing attackers from arbitrarily accessing or exposing the private trajectories. Meanwhile, differential privacy is applied for safeguarding the knowledge transfer process, ensuring that aggregation information from individual teacher models is indistinguishable. Furthermore, we introduce trajectory Equalization and Pseudo Classification as data alignment and accuracy optimization mechanisms for training and aggregating, respectively. Finally, security analysis and extensive experiments indicate that UNIRE framework achieves effective sensitive training data preservation and performance-improved TUL models aggregation, compared to state-of-the-art baselines.