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English(EN) A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Census

新方法将人口普查隐私核算速度提高了1824倍

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的求积法，显著加快了美国十年一次人口普查的隐私核算速度。该方法利用筛子算法和离散傅里叶变换，与现有技术相比实现了1824倍的速度提升，同时符合严格的误差容忍度。这项创新能够更精确地确定差分隐私所需的最小噪声量，从而提高人口普查数据在联邦资金分配和政治重新划分等应用中的统计效用。 AI

影响 通过实现更精确的隐私保证来提高人口普查数据的统计效用，可能改善下游应用。

排序理由 这是一篇研究论文，详细介绍了一种用于人口普查数据隐私核算的新计算方法。[lever_c_demoted from research: ic=2 ai=0.4]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新方法将人口普查隐私核算速度提高了1824倍

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这是一篇研究论文，详细介绍了一种用于人口普查数据隐私核算的新计算方法。[lever_c_demoted from research: ic=2 ai=0.4]
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Buxin Su, Weijie Su, Chendi Wang ·

    一种筛加速求积法用于2020年美国十年期人口普查的精确隐私核算

    arXiv:2606.29835v1 Announce Type: cross Abstract: In 2020, the U.S. Census Bureau adopted differential privacy for the Decennial Census by injecting integer-valued Gaussian noise into published census tabulations. Exactly evaluating the privacy guarantees of these data releases w…

  2. arXiv stat.ML TIER_1 English(EN) · Chendi Wang ·

    一种筛加速求积法用于2020年美国十年期人口普查的精确隐私核算

    In 2020, the U.S. Census Bureau adopted differential privacy for the Decennial Census by injecting integer-valued Gaussian noise into published census tabulations. Exactly evaluating the privacy guarantees of these data releases would enable the Bureau to determine the absolute m…

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