  Membership inference attacks (MIAs) infer whether a specific data record is
used for target model training. MIAs have provoked many discussions in the
information security community since they give rise to severe data privacy
issues, especially for private and sensitive datasets. Knowledge Graphs (KGs),
which describe domain-specific subjects and relationships among them, are
valuable and sensitive, such as medical KGs constructed from electronic health
records. However, the privacy threat to knowledge graphs is critical but rarely
explored. In this paper, we conduct the first empirical evaluation of privacy
threats to knowledge graphs triggered by knowledge graph embedding methods
(KGEs). We propose three types of membership inference attacks: transfer
attacks (TAs), prediction loss-based attacks (PLAs), and prediction
correctness-based attacks (PCAs), according to attack difficulty levels. In the
experiments, we conduct three inference attacks against four standard KGE
methods over three benchmark datasets. In addition, we also propose the attacks
against medical KG and financial KG. The results demonstrate that the proposed
attack methods can easily explore the privacy leakage of knowledge graphs.
