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A systematic evaluation of privacy risks in LLM-enhanced GNNs through a unified framework consisting of five stages and reveals that semantic enrichment amplifies link-, label-, and membership-related signals in the embedding space, making them more exploitable by inference attacks.
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.
PPGNN, a personalized differentially private framework for decentralized graph data, enables user-specific privacy budgets during local perturbation while preserving analytical utility in decentralized graph learning scenarios.