论文标题
在一类定向网络模型中对私人估计进行差异性估计
Differentially private estimation in a class of directed network models
论文作者
论文摘要
尽管已经得出了基于私人双度序列的$ p_0 $模型中的理论属性，但对于以$ p_ {0} $模型为特殊情况的通用定向网络模型，仍然缺乏统一的理论。我们使用流行的Laplace数据释放方法来输出有向网络的双度序列，该序列满足了私人标准 - 不同的隐私。矩的方法用于估计未知参数。我们证明，在某些条件下，差异性私有估计器在某些条件下均匀地一致且渐近地正常。我们的结果由概率模型说明。我们进行仿真研究以说明理论结果并提供真实的数据分析。
Although the theoretical properties in the $p_0$ model based on a differentially private bi-degree sequence have been derived, it is still lack of a unified theory for a general class of directed network models with the $p_{0}$ model as a special case. We use the popular Laplace data releasing method to output the bi-degree sequence of directed networks, which satisfies the private standard--differential privacy. The method of moment is used to estimate unknown parameters. We prove that the differentially private estimator is uniformly consistent and asymptotically normal under some conditions. Our results are illustrated by the Probit model. We carry out simulation studies to illustrate theoretical results and provide a real data analysis.
