AI RESEARCH
Graph Reconstruction from Differentially Private GNN Explanations
arXiv CS.LG
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ArXi:2605.03388v1 Announce Type: new Regulatory frameworks such as GDPR increasingly require that ML predictions be accompanied by post-hoc explanations, even when raw data and trained models cannot be released. Differential privacy (DP) is the standard mitigation for the residual privacy risk of releasing these explanations. We show that DP is not sufficient: an adversary observing only DP-perturbed GNN explanations can reconstruct hidden graph structure with high accuracy.