Locally Differentially Private Graph Embedding
Zening Li Rong-Hua Li Meihao Liao Fusheng Jin Guoren Wang
Graph embedding has been demonstrated to be a powerful tool for learning
latent representations for nodes in a graph. However, despite its superior
performance in various graph-based machine learning tasks, learning over graphs
can raise significant privacy concerns when graph data involves sensitive
information. To address this, in this paper, we investigate the problem of
developing graph embedding algorithms that satisfy local differential privacy
(LDP). We propose LDP-GE, a novel privacy-preserving graph embedding framework,
to protect the privacy of node data. Specifically, we propose an LDP mechanism
to obfuscate node data and adopt personalized PageRank as the proximity measure
to learn node representations. Then, we theoretically analyze the privacy
guarantees and utility of the LDP-GE framework. Extensive experiments conducted
over several real-world graph datasets demonstrate that LDP-GE achieves
favorable privacy-utility trade-offs and significantly outperforms existing
approaches in both node classification and link prediction tasks.