English(EN) Graph Reconstruction from Differentially Private GNN Explanations

新攻击可从GNN解释中重构私有图数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-06 04:00

研究人员开发了一种名为PRIVX的新攻击，可以从差分隐私图神经网络（GNN）解释中重构隐藏的图结构。该攻击利用了高斯差分隐私机制，将重构视为一个逆扩散过程。实验表明，即使在通常部署的隐私预算下，PRIVX也能实现高准确率，这表明仅靠差分隐私可能不足以保护敏感的图数据。 AI

影响证明了在发布GNN解释时，差分隐私可能不足以保护敏感的图数据。

排序理由这是一篇研究论文，详细介绍了针对差分隐私GNN解释的新型攻击。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Rishi Raj Sahoo, Jyotirmaya Shivottam, Subhankar Mishra · 2026-05-06 04:00

从差分隐私GNN解释中进行图重构

arXiv:2605.03388v1 Announce Type: new Abstract: 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 …