PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration
Graph inference over relational data can expose sensitive edge information, and this risk becomes more severe in dynamic graphs, where repeated model updates cause privacy loss to accumulate. We formulate Edge-level Differentially Private Dynamic Graph Inference (EDG) and propose PriDyG, a private inference framework that combines GNN-based structural learning with LLM-based semantic reasoning. PriDyG introduces incremental private multi-hop aggregation, which buffers newly arrived edges and processes each edge exactly once. By parallel composition, the total privacy cost equals that of a single static release, independent of the number or schedule of model updates. Compared with geometri...
| Authors | Yuyang Xia, Ruixuan Liu, Li Xiong |
|---|---|
| Primary category | cs.CR |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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