The invention discloses a track data privacy protection generation method in an Internet of Vehicles environment based on a differential privacy technology and a deep learning optimization method, and belongs to the field of computer science and technology. According to the method, a differential privacy mechanism and an anonymous data collection method are introduced, so that the private information of the user cannot be leaked in the generation and transmission process of the user track data. Through FCN network optimization, the problems of rough and unreasonable paths generated during track generation in a traditional differential privacy method are solved, and a synthetic track highly consistent with an actual track is provided. Experimental results show that the trajectory data generated by the method is remarkably superior to the trajectory data generated by the existing method in the aspects of query error, path rationality and the like, and the method has high practical value. The method not only can be applied to different urban environments, but also can cope with the challenge of large-scale Internet of Vehicles data generation, and has good adaptability and expansibility.
本发明公开了一种是基于差分隐私技术和深度学习优化方法的车联网环境中轨迹数据隐私保护生成方法,属于计算机科学与技术领域。本方法通过引入差分隐私机制与匿名数据收集方法,确保了用户轨迹数据在生成和传输过程中不会泄露用户的私人信息。通过FCN网络优化,解决了传统差分隐私方法在轨迹生成时产生的粗糙和不合理路径问题,提供了与实际轨迹高度一致的合成轨迹。实验结果表明,本发明生成的轨迹数据在查询误差、路径合理性等方面显著优于现有方法,具备较高的实用价值。该方法不仅能够在不同城市环境中应用,还能够应对大规模车联网数据生成的挑战,具有良好的适应性和扩展性。
Internet of vehicles track privacy protection method based on differential privacy and deep learning
一种基于差分隐私与深度学习的车联网轨迹隐私保护方法
2025-05-30
Patent
Electronic Resource
Chinese
A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy
IEEE | 2022
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