基于强化学习与多模态特征融合个性化轨迹发布方法OA

With the widespread adoption of location-based services,trajectory data privacy protection faces challenges of singularity,static mechanisms,and insufficient efficiency.To address these issues,this paper proposes a personalized trajectory publishing method driven by reinforcement learning and multimodal feature fusion.A semantic label generation approach that integrates a Transformer-based bidirectional encoder(bidirectional encoder representations from transformers,BERT)and graph neural networks(GNN)is employed to identify sensitive locations automatically.Furthermore,a deep deterministic policy gradient(DDPG)-based optimization strategy dynamically adjusts privacy weight parameters to achieve an adaptive balance between privacy protection and data utility.In addition,an individualized differential privacy algorithm based on the exponential mechanism is designed to control noise injection according to user-specific privacy levels in an adaptive manner.Comprehensive experiments conducted on real-world datasets demonstrate that,compared with existing mechanisms,the proposed approach improves data utility by 14.85%～53.94%and enhances privacy protection by 10.28%～42.72%,while maintaining high semantic label accuracy.