  Graph Neural Networks (GNNs) have gained significant attention owing to their
ability to handle graph-structured data and the improvement in practical
applications. However, many of these models prioritize high utility
performance, such as accuracy, with a lack of privacy consideration, which is a
major concern in modern society where privacy attacks are rampant. To address
this issue, researchers have started to develop privacy-preserving GNNs.
Despite this progress, there is a lack of a comprehensive overview of the
attacks and the techniques for preserving privacy in the graph domain. In this
survey, we aim to address this gap by summarizing the attacks on graph data
according to the targeted information, categorizing the privacy preservation
techniques in GNNs, and reviewing the datasets and applications that could be
used for analyzing/solving privacy issues in GNNs. We also outline potential
directions for future research in order to build better privacy-preserving
GNNs.
