Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Furthermore, we provide a theoretical analysis of the privacy guarantees and utility offered by the PrivGE framework. Extensive experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility, and significantly outperforms existing methods in node classification and link prediction tasks.

翻译：图嵌入已成为学习图中节点潜在表示的有力工具。尽管在各种基于图的机器学习任务中表现出卓越性能，但当图数据包含个人或敏感信息时，会引发严重的隐私问题。为解决这一问题，我们研究并开发了满足本地差分隐私（LDP）的图嵌入算法。我们提出了一种名为PrivGE的新型隐私保护图嵌入框架，以保护节点数据隐私。具体而言，我们设计了一种LDP机制来混淆节点数据，并利用个性化PageRank作为邻近度度量来学习节点表示。此外，我们对PrivGE框架提供的隐私保障与实用性进行了理论分析。在多个真实世界图数据集上的大量实验表明，PrivGE在隐私与实用性之间实现了最优平衡，并在节点分类和链接预测任务中显著优于现有方法。