English(EN) Provable Privacy Attacks on Trained Shallow Neural Networks

新研究揭示了对浅层神经网络的可证明隐私攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了针对浅层神经网络（特别是两层ReLU网络）的可证明隐私攻击。这些攻击侧重于成员推断和数据重建，表明在某些情况下，可以利用关于隐式偏差的理论结果来高概率地识别训练数据点。这项工作是该隐式偏差驱动背景下首次已知的可证明漏洞。 AI

影响这项研究突显了常见神经网络架构中潜在的隐私漏洞，促使人们进一步研究鲁棒的隐私保护训练方法。

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arXiv cs.LG TIER_1 English(EN) · Guy Smorodinsky, Gal Vardi, Itay Safran · 2026-08-27 04:00

可证明的浅层神经网络训练隐私攻击

arXiv:2410.07632v3 Announce Type: replace Abstract: We study what provable privacy attacks can be shown for trained 2-layer ReLU neural networks, focusing on two types of attacks: membership inference and data reconstruction. We prove that theoretical results on the implicit bias…