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English(EN) Differentially Private Synthetic Data from Sliced Chebyshev Moments

新的SPLICE算法提供与维度无关的DP合成数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的差分隐私合成数据算法，称为SPLICE，即使在高维数据集中也能提供强大的全局分布保证。该算法测量噪声切比雪夫多项式矩，并拟合一个分布以匹配，实现了与目标分布的Sliced Wasserstein-1距离为O(1/sqrt(n))，独立于数据维度。SPLICE的实际实现表明，与现有的差分隐私合成数据方法相比，在基准测试中达到了最先进的性能。 AI

影响 这项研究推进了创建隐私保护合成数据的技术，这对于在不泄露敏感信息的情况下训练AI模型至关重要。

排序理由 这是一篇详细介绍新算法及其理论保证的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的SPLICE算法提供与维度无关的DP合成数据

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这是一篇详细介绍新算法及其理论保证的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lucas Rosenblatt, R. Teal Witter, Christopher Musco ·

    Differentially Private Synthetic Data from Sliced Chebyshev Moments

    arXiv:2610.42081v1 Announce Type: new Abstract: Research on practical differentially private (DP) synthetic data has largely focused on methods that approximately preserve low-dimensional marginals of a target distribution, and can thus only hope to preserve higher-dimensional, g…

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