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.

翻译：图神经网络因其处理图结构数据的能力以及在实践应用中的改进而受到广泛关注。然而，许多模型优先考虑高实用性性能（如准确率），缺乏隐私保护考量，这在隐私攻击猖獗的现代社会中是一个重大问题。为解决此问题，研究人员已开始开发隐私保护型图神经网络。尽管取得了进展，但图领域中的攻击与隐私保护技术仍缺乏全面概述。本综述旨在填补这一空白，依据目标信息总结对图数据的攻击，分类图神经网络中的隐私保护技术，并回顾可用于分析/解决图神经网络隐私问题的数据集与应用。我们同时提出未来研究的潜在方向，以构建更优的隐私保护型图神经网络。