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

• URL: http://arxiv.org/abs/2511.02185v1
• Date: Tue, 04 Nov 2025 01:59:26 GMT
• Title: PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
• Authors: Fuyi Wang, Zekai Chen, Mingyuan Fan, Jianying Zhou, Lei Pan, Leo Yu Zhang,
• Abstract summary: $sysname$ is a lightweight cryptographic scheme for graph-centric inference in the cloud.<n>$sysname$ is carefully designed based on a series of novel 2PC interactive protocols.<n>Experiments across four datasets demonstrate $sysname$'s superior efficiency with $1.3times sim 4.7times$ faster secure predictions.
• Score: 26.99268618245047
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 $1.5\times \sim 1.7\times$ speedups for linear layers and $2\times \sim 15\times$ 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 $1.3\times \sim 4.7\times$ faster secure predictions while maintaining accuracy comparable to plaintext graph property inference.

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