Differentially Private Graph Learning: A Survey
TL;DR
This survey presents the first comprehensive and systematic review of Differentially Private Graph Learning (DPGL), and organizes existing DPGL methods into four categories based on the granularity of privacy protection, namely node-level, edge-level, graph-level, and node-level.
Abstract
Graph learning has been widely applied across diverse domains, including social networks, recommendation systems, and bioinformatics. However, real-world graph data often contains highly sensitive information, raising serious privacy concerns. Differential Privacy (DP) has emerged as a rigorous mathematical framework to protect sensitive information in graph learning while providing formal privacy guarantees. This survey presents the first comprehensive and systematic review of Differentially Private Graph Learning (DPGL). We organize existing DPGL methods into four categories based on the granularity of privacy protection, namely node-level, edge-level, graph-level, and local differential privacy. Within each category, we further analyze learning paradigms, perturbation mechanisms, and their respective strengths and limitations, and identify key technical challenges in the field. Furthermore, we identify future research directions critical for advancing DPGL toward practical deployment in real-world applications. This survey aims to provide a unified reference for researchers and practitioners while inspiring future innovations in privacy-preserving graph learning.