  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.
