A note on differentially private clustering with large additive error
Data Structures and Algorithms
2020-09-29 v1 Machine Learning
Abstract
In this note, we describe a simple approach to obtain a differentially private algorithm for k-clustering with nearly the same multiplicative factor as any non-private counterpart at the cost of a large polynomial additive error. The approach is the combination of a simple geometric observation independent of privacy consideration and any existing private algorithm with a constant approximation.
Cite
@article{arxiv.2009.13317,
title = {A note on differentially private clustering with large additive error},
author = {Huy L. Nguyen},
journal= {arXiv preprint arXiv:2009.13317},
year = {2020}
}