提出可加密计算的图表示学习方法，解决隐私保护与模型性能矛盾
Polynomial Contrastive Learning for Privacy-Preserving Representation Learning on Graphs
- 全多项式设计的GCN编码器与对比损失，兼容同态加密
- 在Cora/CiteSeer/PubMed上性能媲美甚至超越非私有基线
- 适合需要隐私保护的图数据场景，如医疗、金融领域
自监督学习（SSL）已成为无需人工标注即可学习图数据表示的强大范式。然而，主流方法如GRACE因依赖非多项式操作，与同态加密（HE）等隐私保护技术不兼容。本文提出Poly-GRACE，一种面向HE的图自监督学习新框架。该方法包含完全多项式友好的图卷积网络（GCN）编码器和基于多项式的新型对比损失函数。在Cora、CiteSeer和PubMed三个基准数据集上的实验表明，Poly-GRACE不仅支持隐私预训练，且性能高度竞争，在CiteSeer上甚至优于标准非私有基线。本工作推动了实用且高性能的隐私保护图表示学习发展。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations on graph data without requiring manual labels. However, leading SSL methods like GRACE are fundamentally incompatible with privacy-preserving technologies such as Homomorphic Encryption (HE) due to their reliance on non-polynomial operations. This paper introduces Poly-GRACE, a novel framework for HE-compatible self-supervised learning on graphs. Our approach consists of a fully polynomial-friendly Graph Convolutional Network (GCN) encoder and a novel, polynomial-based contrastive loss function. Through experiments on three benchmark datasets -- Cora, CiteSeer, and PubMed -- we demonstrate that Poly-GRACE not only enables private pre-training but also achieves performance that is highly competitive with, and in the case of CiteSeer, superior to the standard non-private baseline. Our work represents a significant step towards practical and high-performance privacy-preserving graph representation learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。