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LLM-enhanced GNNs may boost performance but also expose sensitive information, revealing a stark vulnerability to privacy attacks that traditional models avoid.
Inaudible low-frequency signals can cripple LALM performance by up to 67%, revealing a hidden vulnerability in audio processing systems.
Personalized privacy budgets in decentralized graph learning can significantly enhance data utility while safeguarding sensitive information.
