Locally Private Graph Neural Networks

Understand

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.

• However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information.
• While numerous techniques have been proposed for privacy-preserving deep learning over non-relational data, there is less work addressing the privacy issues pertained to applying deep learning algorithms on graphs.
• In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph.