English(EN) Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy

新方法实现两层ReLU网络的差分隐私

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一种将差分隐私应用于两层ReLU神经网络的方法，这是超越当前凸问题局限性的重要一步。这种新方法利用对偶公式的随机近似来创建一个强凸问题，从而为NoisyCGD等方法提供更准确的隐私边界。实证测试表明，该技术在基准分类任务上实现了与DP-SGD相当的隐私-效用权衡。 AI

影响将差分隐私的应用范围扩展到更复杂的神经网络架构，可能有助于更安全的人工智能开发。

排序理由学术论文，详细介绍了将差分隐私应用于神经网络的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Rob Romijnders, Antti Koskela · 2026-06-16 04:00

用于隐藏状态差分隐私的两层ReLU网络的凸近似

arXiv:2407.04884v4 Announce Type: replace Abstract: The hidden state threat model of differential privacy (DP) assumes that the adversary has access only to the final trained machine learning (ML) model, without seeing intermediate states during training. However, the current pri…