NeurIPS-2021-a-central-limit-theorem-for-differentially-private-query-answering-Paper

Sep 2, 2026

This paper, presented at NeurIPS 2021, focuses on developing a central limit theorem for differentially private query answering. It explores theoretical advancements in privacy-preserving data analysis, specifically addressing how to derive accurate statistical guarantees under differential privacy constraints. The research delves into the complexities of distributing queries in a manner that preserves individual data privacy while maintaining the utility of the aggregated results for analytical purposes. The document likely contains mathematical formulations and proofs to establish the central limit theorem in this specialized context, aiming to provide a more robust understanding of the trade-offs between privacy and accuracy in data analysis. It contributes to the growing body of work on ensuring data confidentiality in machine learning and statistical inference, making it relevant for researchers and practitioners in privacy-preserving machine learning and data science. The findings are crucial for applications requiring sensitive data analysis, such as in healthcare, finance, and government.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

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FAQs

What is the main focus of this research paper?

The paper establishes a central limit theorem for differentially private query answering, contributing to privacy-preserving data analysis.

What are the key challenges addressed in this paper?

It addresses the complexities of distributing queries while preserving individual data privacy and maintaining the utility of aggregate results.

What is the significance of this research?

The findings are crucial for applications requiring sensitive data analysis, offering robust understanding of privacy-accuracy trade-offs.

Sep 2, 2026

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