English(EN) Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability

研究人员探讨图卷积网络中的隐私-效用权衡

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

研究人员开发了一个理论框架，通过检查子采样稳定性来理解图卷积网络（GCNs）中的差分隐私。该研究推导了误分类率的上限，并将其直接与子采样概率联系起来。它还定义了隐私-效用权衡，表明过高或过低的子采样概率可能导致无效的隐私保证或准确性降低。 AI

影响为平衡GCNs中的隐私和效用提供了理论基础，可能指导未来的模型开发。

排序理由学术论文，介绍了GCNs中差分隐私的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 学术论文，介绍了GCNs中差分隐私的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Yexin Zhang, Zhongtian Ma, Qiaosheng Zhang, Zhen Wang · 2026-05-05 04:00

子采样稳定性在图卷积网络中的误分类率与隐私-效用权衡

arXiv:2605.01987v1 Announce Type: new Abstract: We study differential privacy (DP) in Graph Convolutional Networks (GCNs) through the framework of \textit{subsampling stability}. We derive upper bounds on the misclassification rate that depend explicitly on the subsampling probab…