ICLR 2025poster0 citations
Privately Counting Partially Ordered Data
Matthew Joseph, Mónica Ribero, Alexander Yu
Abstract
We consider differentially private counting when each data point consists of $d$ bits satisfying a partial order. Our main technical contribution is a problem-specific $K$-norm mechanism that runs in time $O(d^2)$. Experiments show that, depending on the partial order in question, our solution dominates existing pure differentially private mechanisms and can reduce their error by an order of magnitude or more.
differential privacy
BibTeX
@inproceedings{
joseph2025privately,
title={Privately Counting Partially Ordered Data},
author={Matthew Joseph and M{\'o}nica Ribero and Alexander Yu},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=hVTaXJ0I5M}
}