论文标题
特权：通过自适应马尔可夫模型差异私人轨迹合成
PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model
论文作者
论文摘要
发布轨迹数据（个人的运动信息）非常有用，但也引起了隐私问题。为了处理隐私问题，在本文中，我们应用差异隐私，数据隐私的标准技术以及马尔可夫链模型，以生成合成轨迹。我们注意到现有研究都使用马尔可夫链模型，因此提出了一个框架来分析马尔可夫链模型在此问题中的使用。基于分析，我们提出了一种有效的算法特权，该算法适应一阶和二阶Markov模型。我们评估了有关合成和现实世界数据集的特殊迹象和现有方法，以证明我们方法的优越性。
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.
