(2310.11060) Privacy-preserving graph embedding based on local differential privacy

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Abstract:Graph embedding has emerged as 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 issues arise when the graph data contains personal or sensitive information. To address this problem, we study and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, called PrivGE, to protect the privacy of node data. Specifically, we propose an LDP mechanism to obfuscate node data and use personalized PageRank as a 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 multiple 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.