RDPP: Vehicle Trajectory Data Protection Scheme Combining Regional Realizability and Deep Learning

With the rapid development of the Internet of Vehicles (IoV) and location‐based services (LBS), the privacy and security of trajectory data have become a top priority. Disclosure of trajectory privacy may pose many risks to users. To solve this problem, this paper proposes a vehicle trajectory data protection scheme combining regional realizability and deep learning (RDPP). Firstly, a regional realizability processing is proposed, which divides and covers geographical areas according to road network density and then defines the trajectory generation restrictions. Secondly, this paper proposed a combined regional realizability of the trajectory data generation model (RRP‐TrajGAN) that can combine the trajectory generation restrictions to generate trajectory data that is in line with the real situation. Finally, the proposed personalized privacy budget allocation method based on the clustering and density method (CD‐DP) is used to cluster the generated trajectory data, and a reasonable privacy budget is allocated to the trajectory data according to the clustering density attribute. Compared with more advanced schemes, this paper's approach uniquely combines regional realizability processing with deep generative models and density‐based privacy budget allocation, achieving a balance between privacy and utility without sacrificing real‐world feasibility. The experimental results show that compared with other existing schemes, the proposed scheme's degree of privacy protection is improved by 11.88%–39.82%, while data availability can be well guaranteed. In addition, the time complexity of the proposed scheme is , which is better than the comparison scheme.