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English(EN) Can R\'enyi Accountants Beat Lossless Accountants for DP-SGD?

Rényi DP会计师在DP-SGD方面显示出改进的隐私保证

一篇新的研究论文探讨了Rényi DP会计师在差分隐私随机梯度下降(DP-SGD)中的有效性。研究观察到,Rényi DP可以利用与无损框架不同的最坏情况分布对,特别是二维高斯混合模型。数值和渐近比较表明,这种改进的Rényi DP界限可以在各种超参数设置下提供更优越的隐私保证。 AI

影响 这项研究可能带来更有效和高效的隐私保护机器学习技术。

排序理由 在arXiv上发表的研究论文,详细介绍了一种新的差分隐私会计方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Rényi DP会计师在DP-SGD方面显示出改进的隐私保证

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在arXiv上发表的研究论文,详细介绍了一种新的差分隐私会计方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jinho Bok ·

    R\'enyi 账户能否在 DP-SGD 上击败无损账户?

    arXiv:2610.17120v1 Announce Type: cross Abstract: Recent frameworks in differential privacy (DP), such as privacy loss distributions and $f$-DP, provide lossless representations of the privacy leakage of mechanisms. These approaches are often considered to yield tighter $(\vareps…