为图神经网络设计高效安全推理方案，速度提升1.5倍以上。
PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
- 结合加法与函数秘密共享的两方安全计算协议
- 线性层提速1.5~1.7倍，非线性层提速2~15倍
- 适合隐私敏感场景下的云上图数据安全推理
图神经网络（GNN）是分析图结构数据的强大工具，广泛应用于各类服务。在隐私敏感的云环境中部署此类服务，需开发保护敏感图数据的安全推理（SI）协议。然而，现有方案多聚焦于图像和文本的卷积模型，对图神经网络及图数据的安全性研究相对不足。本文设计、实现并评估了$/sysname$，一种面向云端图中心推理的轻量级密码方案。通过在两方安全计算（2PC）中融合加法与函数秘密共享，$/sysname$基于一系列新颖的2PC交互协议，使线性层加速1.5~1.7倍，非线性层加速2~15倍，优于当前最优方案。理论分析证明其正确性、安全性与轻量化特性。在四个数据集上的大量实验表明，$/sysname$在保持与明文图属性推断相当精度的前提下，安全预测速度提升1.3~4.7倍。
原文摘要 · Abstract (English)
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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