PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks

Graph neural networks (GNNs) are powerful tools for analyzing and learning from graph-structured (GS) data, facilitating a wide range of services. Deploying such services in privacy-critical cloud environments necessitates the development of secure inference (SI) protocols that safeguard sensitive GS data. However, existing SI solutions largely focus on convolutional models for image and text data, leaving the challenge of securing GNNs and GS data relatively underexplored. In this work, we design, implement, and evaluate sysname, a lightweight cryptographic scheme for graph-centric inference in the cloud. By hybridizing additive and function secret sharings within secure two-party computation (2PC), sysname is carefully designed based on a series of novel 2PC interactive protocols that achieve 1.5× ~ 1.7× speedups for linear layers and 2× ~ 15× for non-linear layers over state-of-the-art (SotA) solutions. A thorough theoretical analysis is provided to prove sysname's correctness, security, and lightweight nature. Extensive experiments across four datasets demonstrate sysname's superior efficiency with 1.3× ~ 4.7× faster secure predictions while maintaining accuracy comparable to plaintext graph property inference.