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

arXiv: https://arxiv.org/abs/2511.02185 · HTML: https://mlsanity.com/paper/2511.02185/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [CrypTFlow2: Practical 2-Party Secure Inference](https://mlsanity.com/paper/2010.06457/) (2020)
- [LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis](https://mlsanity.com/paper/2108.06504/) (2021)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [XONN: XNOR-based Oblivious Deep Neural Network Inference](https://mlsanity.com/paper/1902.07342/) (2019)
- [SIRNN: A Math Library for Secure RNN Inference](https://mlsanity.com/paper/2105.04236/) (2021)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service](https://mlsanity.com/paper/2202.07835/) (2022)
- [CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference](https://mlsanity.com/paper/2209.11904/) (2022)
- [PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2305.16163/) (2023)
- [Communication-Efficient (Client-Aided) Secure Two-Party Protocols and Its Application](https://mlsanity.com/paper/1907.03415/) (2019)
- [Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning](https://mlsanity.com/paper/2210.01988/) (2022)

## Abstract (arXiv)

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.