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

新方法实现2020年美国人口普查数据精确隐私核算

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-29 06:19

研究人员开发了一种新的求积方法，用于精确计算2020年美国十年期人口普查数据的隐私保证。该方法使用离散傅里叶变换和筛子算法来加速计算，比以前的技术快1824倍。这项创新实现了精确的隐私核算，确保数据添加的噪声最小化，从而提高了其在资金分配和重新划分选区等应用中的统计效用。 AI

影响通过优化隐私保护噪声注入，提高了关键应用的数据效用。

排序理由该集群包含一篇学术论文，详细介绍了一种新的隐私核算计算方法。[lever_c_demoted from research: ic=1 ai=0.4]

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

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Buxin Su, Weijie Su, Chendi Wang · 2026-06-30 04:00

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

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…
arXiv stat.ML TIER_1 English(EN) · Chendi Wang · 2026-06-29 06:19

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

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…