Distributed Private Aggregation in Graph Neural Networks

Graph Neural Networks (GNNs) have shown considerable promise in handling graph-structured data, yet their use is restricted in privacy-sensitive environments, especially in distributed settings. In this setting, current methods for preserving privacy in GNNs often rely on unrealistic assumptions or fail to construct effective models. In response, this paper introduces Distributed Private Aggregation (DPA), a pioneering GNN aggregation method which is built upon Secure Multi-Party Computation protocols, and is designed to ensure node-level differential privacy. We implement DPA-GNN, which to our knowledge, is the most effective privacy-preserving GNN model suitable for distributed contexts. Through extensive experiments on six real-world datasets, DPA-GNN has proven to consistently surpass existing privacy preserving GNNs, offering an optimal balance between privacy and utility.