SecGAT: Efficient and Privacy-Preserving Graph Attention Network Inference in Cloud

Abstract

Deploying outsourced graph neural network (GNN) inference services in the cloud is gaining widespread application across various fields, such as fraud detection and social network analysis. Cloud servers utilize outsourced model to analyze the graph data of data owners, enabling data owners to enjoy high-quality GNN inference services. However, this approach leads to privacy concerns regarding GNN models, graph data and inference results. To address the privacy issues, some privacy-preserving GNN inference schemes have been proposed. But the existing schemes are only applicable to graph convolutional network and not to graph attention network (GAT) with stronger expressive power. Therefore, we propose a secure GAT inference scheme (SecGAT) for outsourcing scenarios. First, we represent the Beaver triple-based multiplication process as a two-phase multiplication, which allows us to combine specific algorithms to optimize the communication overhead. Then, we design a graph data encryption method to protect the privacy of outsourced graph data. Finally, we propose a series of customized algorithms for secure GAT inference. Based on the proposed building blocks, we construct a complete GAT inference process. Rigorous security analysis and extensive evaluations demonstrate the effectiveness of our scheme. By comparing the core algorithms, our scheme can improve computational efficiency by more than 20% and reduce communication overhead by 20%−40% compared to existing schemes.