arXiv:2605.30600v1 Announce Type: new Abstract: Randomized sketching is a central tool for compressing large-scale optimization problems while preserving accuracy. In particular, sketches that are based on structured matrices, such as the Hadamard matrix, can be applied efficiently and often yield solutions that approximate those of the original problem at much lower computational cost. In differential privacy (DP), Gaussian sketching has been used to solve DP linear regression, beginning with \citet{sheffet2017differentially, sheffet2019old} and later refined by \citet{lev2025gaussianmix, lev
The research on efficient and accurate methods for differential privacy is critically important as AI deployment scales across sensitive applications, necessitating robust privacy guarantees.
This work advances the mathematical and algorithmic foundations of differential privacy, a key enabler for secure and ethical AI, impacting data utility and regulatory compliance.
Improved Gaussian sketching methods offer more efficient and accurate ways to apply differential privacy to large-scale optimization problems, potentially reducing computational overhead and improving model performance under privacy constraints.
- · AI researchers
- · Data privacy startups
- · Cloud computing providers
- · Healthcare and finance sectors
- · Organizations with weak privacy practices
More widespread and efficient adoption of differentially private machine learning models in sensitive domains.
Reduced risk of data breaches and increased public trust in AI applications handling personal information.
New regulatory standards and industry best practices emerge, mandating advanced privacy-preserving techniques like those improved upon here.
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Read at arXiv cs.LG