MDP: Privacy-Preserving GNN Based on Matrix Decomposition and Differential Privacy

In recent years, graph neural networks (GNN) have developed rapidly in various fields, but the high computational consumption of its model training often discourages some graph owners who want to train GNN models but lack computing power. Therefore, these data owners often cooperate with external calculators during the model training process, which will raise critical severe privacy concerns. Protecting private information in graph, however, is difficult due to the complex graph structure consisting of node features and edges. To solve this problem, we propose a new privacy-preserving GNN named MDP based on matrix decomposition and differential privacy (DP), which allows external calculators train GNN models without knowing the original data. Specifically, we first introduce the concept of topological secret sharing (TSS), and design a novel matrix decomposition method named eigenvalue selection (ES) according to TSS, which can preserve the message passing ability of adjacency matrix while hiding edge information. We evaluate the feasibility and performance of our model through extensive experiments, which demonstrates that MDP model achieves accuracy comparable to the original model, with practically affordable overhead.