# A note on differentially private clustering with large additive error

arXiv: https://arxiv.org/abs/2009.13317 · HTML: https://mlsanity.com/paper/2009.13317/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Differentially-Private Clustering of Easy Instances](https://mlsanity.com/paper/2112.14445/) (2021)
- [Differential Privacy for Clustering Under Continual Observation](https://mlsanity.com/paper/2307.03430/) (2023)
- [Differentially Private Clustering in Data Streams](https://mlsanity.com/paper/2307.07449/) (2023)
- [Making Old Things New: A Unified Algorithm for Differentially Private Clustering](https://mlsanity.com/paper/2406.11649/) (2024)

## Abstract (arXiv)

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.