Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their vulnerability to privacy inference attacks restricts their practicality, especially in high-stake domains. To address this issue, privacy-preserving GNNs have been proposed, focusing on preserving node and/or link privacy. This work takes a step back and investigates how GNNs contribute to privacy leakage. Through theoretical analysis and simulations, we identify message passing under structural bias as the core component that allows GNNs to \textit{propagate} and \textit{amplify} privacy leakage. Building upon these findings, we propose a principled privacy-preserving GNN framework that effectively safeguards both node and link privacy, referred to as dual-privacy preservation. The framework comprises three major modules: a Sensitive Information Obfuscation Module that removes sensitive information from node embeddings, a Dynamic Structure Debiasing Module that dynamically corrects the structural bias, and an Adversarial Learning Module that optimizes the privacy-utility trade-off. Experimental results on four benchmark datasets validate the effectiveness of the proposed model in protecting both node and link privacy while preserving high utility for downstream tasks, such as node classification.

翻译：图神经网络（GNNs）是学习图结构数据（如社交网络）表示的强大工具。然而，它们对隐私推理攻击的脆弱性限制了其实用性，尤其是在高风险领域。为解决这一问题，研究者提出了隐私保护型GNN，重点保护节点和/或链路隐私。本研究回溯根本，探究GNN如何导致隐私泄露。通过理论分析与模拟，我们识别出结构偏置下的消息传递是让GNN能够“传播”并“放大”隐私泄露的核心机制。基于这些发现，我们提出一个原则性的隐私保护型GNN框架，该框架能有效保障节点和链路隐私，即实现双重隐私保护。该框架包含三个主要模块：敏感信息混淆模块（从节点嵌入中移除敏感信息）、动态结构去偏模块（动态修正结构偏置）以及对抗学习模块（优化隐私-效用权衡）。在四个基准数据集上的实验结果验证了所提模型在保护节点与链路隐私方面的有效性，同时确保了下游任务（如节点分类）的高效用性。