Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications

Knowledge Graph (KG) has attracted more and more companies’ attention for its ability to connect different types of data in meaningful ways and support rich data services. However, due to privacy concerns, different companies cannot share their own KGs with each other. Such data isolation problem limits the performance of KG and prevents its further development. Therefore, how to let multiple parties conduct KG-related tasks collaboratively on the basis of privacy protection becomes an important research question to answer. In this paper, to fill this gap, we summarize the open problems for privacy-preserving KG in the data isolation setting and propose possible solutions for them. Specifically, we summarize the open problems in privacy-preserving KG from four aspects, i.e., merging, query, representation, and completion. We present these problems in detail and propose possible technical solutions for them, along with the datasets, evaluation methods, and future research directions. We also provide three privacy-preserving KG application scenarios.