Graph neural networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks by effectively modeling high-order interactions between nodes. However, training GNNs without protection may leak sensitive personal information in graph data, including links and node features. Local differential privacy (LDP) is an advanced technique for protecting data privacy in decentralized networks. Unfortunately, existing local differentially private GNNs either only preserve link privacy or suffer significant utility loss in the process of preserving link and node feature privacy. In this paper, we propose an effective LDP framework, called HoGS, which trains GNNs with link and feature protection by generating a synthetic graph. Concretely, HoGS first collects the link and feature information of the graph under LDP, and then utilizes the phenomenon of homophily in graph data to reconstruct the graph structure and node features separately, thereby effectively mitigating the negative impact of LDP on the downstream GNN training. We theoretically analyze the privacy guarantee of HoGS and conduct experiments using the generated synthetic graph as input to various state-of-the-art GNN architectures. Experimental results on three real-world datasets show that HoGS significantly outperforms baseline methods in the accuracy of training GNNs.

翻译：图神经网络（GNNs）通过有效建模节点间的高阶交互，在各种基于图的机器学习任务中展现出卓越性能。然而，未经保护的GNN训练可能泄露图数据中的敏感个人信息，包括链接和节点特征。本地差分隐私（LDP）是保护去中心化网络中数据隐私的先进技术。遗憾的是，现有的本地差分隐私GNN方法要么仅保护链接隐私，要么在同时保护链接和节点特征隐私的过程中遭受显著的效用损失。本文提出一种有效的LDP框架——HoGS，该框架通过生成合成图来训练具有链接和特征保护的GNN。具体而言，HoGS首先在LDP约束下收集图的链接和特征信息，随后利用图数据中的同质性现象分别重构图结构和节点特征，从而有效缓解LDP对下游GNN训练的负面影响。我们从理论上分析了HoGS的隐私保障能力，并利用生成的合成图作为多种先进GNN架构的输入进行实验。在三个真实数据集上的实验结果表明，HoGS在训练GNN的准确率方面显著优于基线方法。