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New method enables exact privacy accounting for 2020 US Census data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new quadrature method to precisely calculate privacy guarantees for the 2020 U.S. Decennial Census data. This method uses discrete Fourier transforms and a sieve algorithm to accelerate computations, achieving a 1,824-fold speedup over previous techniques. The innovation allows for exact privacy accounting, ensuring minimal noise is added to the data, thereby enhancing its statistical utility for applications like funding allocation and redistricting. AI

IMPACT Enhances data utility for critical applications by optimizing privacy-preserving noise injection.

RANK_REASON The cluster contains an academic paper detailing a new computational method for privacy accounting. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method enables exact privacy accounting for 2020 US Census data

COVERAGE [2]

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

    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…

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

    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…

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