基于差分隐私模型的位置轨迹发布技术研究

位置轨迹大数据的安全分享、发布需求离不开位置轨迹隐私保护技术支持。在差分隐私出现之前，K-匿名及其衍生模型为位置轨迹隐私保护提供了一种量化评估的手段，但其安全性严重依赖于攻击者所掌握的背景知识，当有新的攻击出现时模型无法提供完善的隐私保护。差分隐私技术的出现有效地弥补了上述问题，越来越多地应用于轨迹数据隐私发布领域中。该文对基于差分隐私理论的轨迹隐私保护技术进行了研究与分析，重点介绍了差分隐私模型下位置直方图、轨迹直方图等空间统计数据发布方法，差分隐私模型下轨迹数据集发布方法，以及连续轨迹实时发布隐私保护模型。与此同时，在对现有方法对比分析的基础上，提出了未来的重点发展方向。

Research on Differentially Private Trajectory Data Publishing

Securely sharing and publishing location trajectory data relies on support of location privacy protection technology. Prior to the advent of differential privacy, K-anonymity and its derived models provide a means of quantitative assessment of location-trajectory privacy protection. However, its security relies heavily on the background knowledge of the attacker, and the model can not provide perfect privacy protection when a new attack occurs. Differential privacy effectively compensates for the above problems, and it proves the level of privacy protection based on rigorous mathematical theory and is increasingly used in the field of trajectory data privacy publishing. Therefore, the trajectory privacy protection technology based on differential privacy theory is studied and analyzed, and the methods of spatial statistical data publishing are introduced such as location histogram and trajectory histogram, the method of trajectory data set publishing and the model of continuous real-time location release privacy protection. At the same time, the existing methods are compared and analyzed, the key development directions are put forward in the future.