论文标题
朝着实用的差异私人因果图发现
Towards practical differentially private causal graph discovery
论文作者
论文摘要
因果图发现是指从纯观察数据中发现因果关系图的过程。像其他统计数据一样，因果图可能会泄漏有关数据集中参与者的敏感信息。在本文中，我们提出了一种私人的因果图发现算法，即PRIV-PC，该算法可改善与最新技术相比的实用程序和运行时间。 PRIV-PC的设计遵循一种名为Sieve和Ob-Oxamine的新颖范式，该范式使用少量的隐私预算来过滤“微不足道的”查询，并利用其余预算来为“重要”查询获得高度准确的答案。我们还对有条件的独立性测试进行了首次灵敏度分析，包括有条件的肯德尔的tau和有条件的Spearman的Rho。我们在4个公共数据集上评估了priv-pc，并将其与最先进的数据进行了比较。结果表明，PRIV-PC实现10.61至32.85倍的速度和更好的实用程序。
Causal graph discovery refers to the process of discovering causal relation graphs from purely observational data. Like other statistical data, a causal graph might leak sensitive information about participants in the dataset. In this paper, we present a differentially private causal graph discovery algorithm, Priv-PC, which improves both utility and running time compared to the state-of-the-art. The design of Priv-PC follows a novel paradigm called sieve-and-examine which uses a small amount of privacy budget to filter out "insignificant" queries, and leverages the remaining budget to obtain highly accurate answers for the "significant" queries. We also conducted the first sensitivity analysis for conditional independence tests including conditional Kendall's tau and conditional Spearman's rho. We evaluated Priv-PC on 4 public datasets and compared with the state-of-the-art. The results show that Priv-PC achieves 10.61 to 32.85 times speedup and better utility.
