Optimal Differentially Private Sampling of Unbounded Gaussians

Valentio Iverson, Gautam Kamath, Argyris Mouzakis
3/3/2025
cs.DScs.CRcs.IT

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Abstract

We provide the first $\widetilde{\mathcal{O}}\left(d\right)$-sample algorithm for sampling from unbounded Gaussian distributions under the constraint of $\left(\varepsilon, δ\right)$-differential privacy. This is a quadratic improvement over previous results for the same problem, settling an open question of Ghazi, Hu, Kumar, and Manurangsi.

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Cite this paper

@article{iverson2025optimal,
  title  = {Optimal Differentially Private Sampling of Unbounded Gaussians},
  author = {Valentio Iverson and Gautam Kamath and Argyris Mouzakis},
  year   = {2025},
  eprint = {2503.01766},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.01766v1}
}

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