# Graph Retrieval-Augmented Generation

GraphRAG links information across documents through entity relations, but black-box queries can also be used to reconstruct the knowledge graph, leaking whether entities are connected and what those relations mean. RelGuard rewrites source documents before indexing: it generates candidate relations from entity types, then uses an exponential mechanism to sample a replacement predicate or delete each relation, with a relation-level differential privacy guarantee. Across LightRAG, PathRAG and HippoRAG 2, two datasets and two reconstruction attacks, its relation-recovery F1 is 26.98 to 44.35 percentage points below the strongest baseline, while the extra question-answering F1 loss is 0.59 to 4.78 points in most settings.

GraphRAG links information across documents through entity relations, but black-box queries can also be used to reconstruct the knowledge graph, leaking whether entities are connected and what those relations mean. RelGuard rewrites source documents before indexing: it generates candidate relations from entity types, then uses an exponential mechanism to sample a replacement predicate or delete each relation, with a relation-level differential privacy guarantee. Across LightRAG, PathRAG and HippoRAG 2, two datasets and two reconstruction attacks, its relation-recovery F1 is 26.98 to 44.35 percentage points below the strongest baseline, while the extra question-answering F1 loss is 0.59 to 4.78 points in most settings.