You can preserve graph privacy without destroying utility by selectively removing edges based on their likelihood rather than adding uniform noise everywhere—EdgeRefine improves accuracy by 17-20% over baselines while maintaining strong privacy guarantees.
EdgeRefine tackles privacy leaks in Graph Neural Networks by using Jaccard similarity to intelligently sample edges while maintaining differential privacy. Instead of adding noise uniformly across all edges, it estimates which edges are most likely real and strategically removes false edges, achieving much better accuracy-privacy trade-offs than existing methods.