NeurIPS 2018spotlight59 citations
Differentially Private k-Means with Constant Multiplicative Error
Abstract
We design new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy. In both models, our algorithms achieve significantly improved error guarantees than the previous state-of-the-art. In addition, in the local model, our algorithm significantly reduces the number of interaction rounds.
BibTeX
@inproceedings{NEURIPS2018_32b991e5,
author = {Stemmer, Uri and Kaplan, Haim},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Differentially Private k-Means with Constant Multiplicative Error},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/32b991e5d77ad140559ffb95522992d0-Paper.pdf},
volume = {31},
year = {2018}
}