arXiv Machine Learning
Auditing Privacy Risks in LLM-Enhanced Graph Neural Networks
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Are LLM-Enhanced GNNs Privacy-Safe?
The paper evaluates privacy risks in graph neural networks enhanced by large language models (LLMs). Using a five‑stage framework, the authors test six real‑world text‑attributed graph datasets with 42 model configurations and six privacy attack methods across link, label, and membership inference threats. Results show that LLM‑enhanced GNNs are more vulnerable than shallow baselines, with semantic enrichment amplifying exploitable signals, and that differential privacy can reduce risk but at a significant cost to utility.
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Rethinking Generative Reconstruction Attacks against Graph Neural Network Models
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Bayesian Membership Privacy for Graph Neural Networks
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Model Inversion Attacks: A Survey of Approaches and Countermeasures
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arXiv Machine Learning
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