Introduces incremental private multi-hop aggregation so that edge-level differential privacy cost stays constant across arbitrarily many updates to a dynamic graph, plus an LLM branch that adds utility at no extra privacy cost.
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PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration
Introduces incremental private multi-hop aggregation so that edge-level differential privacy cost stays constant across arbitrarily many updates to a dynamic graph, plus an LLM branch that adds utility at no extra privacy cost.