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English(EN) One-Shot Private Confidence Regions via Resampling

新方法实现单次差分私有置信区域

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的框架，可以用一次隐私成本创建差分私有置信区域。该方法仅向最终重采样分位数添加噪声，而不是对每个中间估计器进行私有化。新程序通过降低隐私成本提供了显著优势，该成本对于有放回抽样来说是重采样数量的对数，而对于子抽样来说则与重采样数量无关。该方法为包括均值、分位数和退化U统计量在内的各种估计器提供了非渐近高斯差分隐私和效用保证，为执行差分隐私不确定性量化提供了一种更有效的方法。 AI

影响 提高了AI模型开发和分析中使用的统计方法的隐私保证。

排序理由 详细介绍新统计方法的学术论文。[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 →

新方法实现单次差分私有置信区域

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详细介绍新统计方法的学术论文。[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) · Shourya Pandey, Purnamrita Sarkar, Po-Ling Loh, Debepsita Mukherjee ·

    通过重采样实现单次私有置信区域

    arXiv:2610.08460v1 Announce Type: new Abstract: We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final resampling quantile instead of privatizing the estimator computed on each resampl…

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