AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
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.
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