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Preprint Aug 2026

Are LLM-Enhanced GNNs Privacy-Safe?

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.

Longzhu He, Zekun Wen, Chaozhuo Li et al. · 0 citations