Weighted Fourier Factorizations: Optimal Gaussian Noise for Differentially Private Marginal and Product Queries

This article, sourced from ArXiv, likely presents a novel approach to differentially private data analysis. The title suggests a focus on optimizing the addition of Gaussian noise, a common technique for achieving differential privacy, in the context of marginal and product queries. The use of "Weighted Fourier Factorizations" indicates a potentially sophisticated mathematical framework. The research likely aims to improve the accuracy and utility of private data analysis by minimizing the noise added while still maintaining privacy guarantees.

• •Focuses on differentially private data analysis.
• •Uses Weighted Fourier Factorizations.
• •Optimizes Gaussian noise for marginal and product queries.
• •Aims to improve accuracy and utility while maintaining privacy.