  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.
