Knowledge graphs have become increasingly popular supplemental information because they represented structural relations between entities. Knowledge graph embedding methods (KGE) are used for various downstream tasks, e.g., knowledge graph completion, including triple classification, link prediction. However, the knowledge graph also includes much sensitive information in the training set, which is very vulnerable to privacy attacks. In this paper, we conduct such one attack, i.e., membership inference attack, on four standard KGE methods to explore the privacy vulnerabilities of knowledge graphs. Our experimental results on four benchmark knowledge graph datasets show that our privacy attacks can reveal the membership information leakage of KGE methods.

翻译：知识图因代表各实体之间的结构关系而越来越受欢迎的补充信息,知识图嵌入方法(KGE)用于各种下游任务,例如知识图的完成,包括三级分类、链接预测,但是,知识图也包含培训成套材料中的许多敏感信息,这极易受到隐私攻击的伤害。在本文件中,我们对四种标准的KGE方法进行这种攻击,即成员推论攻击,以探索知识图的隐私脆弱性。我们在四个基准知识图数据集上的实验结果显示,我们的隐私攻击可以揭示KGE方法的成员信息泄漏。