Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
This survey addresses the critical problem of data isolation for companies and organizations that want to leverage KGs collaboratively without compromising privacy, which is a foundational challenge for multi-party KG applications.
This paper surveys the open problems in privacy-preserving Knowledge Graphs (KGs) when multiple parties cannot share their KGs due to data isolation. It identifies challenges in merging, querying, representation, and completion of KGs under privacy constraints and proposes potential solutions.
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, the data isolation problem limits the performance of KG and prevents its further development. That is, multiple parties have their own KGs but they cannot share with each other due to regulation or competition reasons. Therefore, how to conduct privacy preserving KG becomes an important research question to answer. That is, multiple parties conduct KG related tasks collaboratively on the basis of protecting the privacy of multiple KGs. To date, there is few work on solving the above KG isolation problem. In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in 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 details and propose possible technical solutions for them. Moreover, we present three privacy preserving KG-aware applications and simply describe how can our proposed techniques be applied into these applications.