Publishing trajectory data (individual's movement information) is very useful, but it also raises privacy concerns. To handle the privacy concern, in this paper, we apply differential privacy, the standard technique for data privacy, together with Markov chain model, to generate synthetic trajectories. We notice that existing studies all use Markov chain model and thus propose a framework to analyze the usage of the Markov chain model in this problem. Based on the analysis, we come up with an effective algorithm PrivTrace that uses the first-order and second-order Markov model adaptively. We evaluate PrivTrace and existing methods on synthetic and real-world datasets to demonstrate the superiority of our method.

翻译：出版轨迹数据(个人流动信息)非常有用,但也引起了隐私问题。为了处理隐私问题,我们在本文中应用了差异隐私、数据隐私标准技术以及Markov 链式模型来生成合成轨迹。我们注意到,现有的研究都使用了Markov 链式模型,从而提出了一个框架来分析Markov 链式模型在此问题上的使用情况。根据分析,我们提出了一个有效的算法PrivTrace,该算法使用第一级和第二级Markov 模型适应性。我们评估了PrivTrace以及合成和真实世界数据集的现有方法,以展示我们方法的优越性。