Privacy-Preserving Graph Embedding based on Local Differential Privacy

Understand

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.