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（GNNs）因其处理图结构数据的能力以及在实践应用中的提升而受到广泛关注。然而，许多模型优先追求高实用性性能（如准确率），缺乏对隐私的考虑，而在隐私攻击猖獗的现代社会，这是一个主要问题。为解决这一问题，研究人员已开始开发隐私保护型GNNs。尽管取得了一定进展，但在图领域，关于攻击与隐私保护技术的全面综述仍然匮乏。本综述旨在填补这一空白，根据目标信息对图数据攻击进行总结，对GNNs中的隐私保护技术进行分类，并综述可用于分析/解决GNNs隐私问题的数据集与应用。此外，我们还概述了未来研究可能的方向，以构建更优的隐私保护型GNNs。