Weighted Fourier Factorizations: Optimal Gaussian Noise for Differentially Private Marginal and Product Queries
Published:Dec 25, 2025 04:14
•1 min read
•ArXiv
Analysis
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.
Key Takeaways
Reference
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