Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined privacy analysis that yields significantly tighter bounds than prior results. We then demonstrate how this improved analysis leads to performance improvement in different linear regression settings, establishing theoretical utility guarantees. Empirically, our methods improve performance across multiple datasets and, in several cases, reduce runtime.
View on arXiv@article{lev2025_2505.24603,
title={ The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches },
author={ Omri Lev and Vishwak Srinivasan and Moshe Shenfeld and Katrina Ligett and Ayush Sekhari and Ashia C. Wilson },
journal={arXiv preprint arXiv:2505.24603},
year={ 2025 }
}