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New method enables one-shot differentially private confidence regions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for creating differentially private confidence regions with a single privacy cost. This method involves adding noise only to the final resampling quantile, rather than privatizing each intermediate estimator. The new procedure offers significant advantages by reducing the privacy cost, which is logarithmic in the number of resamples for with-replacement sampling and independent of the number of resamples for subsampling. This approach provides non-asymptotic Gaussian Differential Privacy and utility guarantees for various estimators, including means, quantiles, and degenerate U-statistics, offering a more efficient way to perform DP uncertainty quantification. AI

IMPACT Improves privacy guarantees for statistical methods used in AI model development and analysis.

RANK_REASON Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method enables one-shot differentially private confidence regions

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Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Shourya Pandey, Purnamrita Sarkar, Po-Ling Loh, Debepsita Mukherjee ·

    One-Shot Private Confidence Regions via Resampling

    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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