Author

Chaozhuo Li

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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

Jul 2026

Toward Personalized Differentially Private Learning for Decentralized Local Graphs

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.

Longzhu He, Peng Tang, Chaozhuo Li et al. · IEEE Transactions on Knowled... · 0 citations