ICML 2025poster0 citations
Approximate Differential Privacy of the $\ell_2$ Mechanism
Matthew Joseph, Alex Kulesza, Alexander Yu
Abstract
We study the $\ell_2$ mechanism for computing a $d$-dimensional statistic with bounded $\ell_2$ sensitivity under approximate differential privacy. Across a range of privacy parameters, we find that the $\ell_2$ mechanism obtains error approaching that of the Laplace mechanism as $d \to 1$ and approaching that of the Gaussian mechanism as $d \to \infty$; however, it dominates both in between.
differential privacy
BibTeX
@inproceedings{
joseph2025approximate,
title={Approximate Differential Privacy of the \${\textbackslash}ell\_2\$ Mechanism},
author={Matthew Joseph and Alex Kulesza and Alexander Yu},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=ypeehAYK7W}
}