Polynomial Contrastive Learning for Privacy-Preserving Representation Learning on Graphs

• URL: http://arxiv.org/abs/2509.25205v1
• Date: Fri, 19 Sep 2025 20:00:30 GMT
• Title: Polynomial Contrastive Learning for Privacy-Preserving Representation Learning on Graphs
• Authors: Daksh Pandey,
• Abstract summary: Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations on graph data without requiring manual labels.<n>Leading SSL methods like GRACE are fundamentally incompatible with privacy-preserving technologies such as Homomorphic Encryption (HE)<n>This paper introduces Poly-GRACE, a novel framework for HE-compatible self-supervised learning on graphs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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