Locating a Small Cluster Privately
Data Structures and Algorithms
2017-03-14 v2 Cryptography and Security
Machine Learning
Abstract
We present a new algorithm for locating a small cluster of points with differential privacy [Dwork, McSherry, Nissim, and Smith, 2006]. Our algorithm has implications to private data exploration, clustering, and removal of outliers. Furthermore, we use it to significantly relax the requirements of the sample and aggregate technique [Nissim, Raskhodnikova, and Smith, 2007], which allows compiling of "off the shelf" (non-private) analyses into analyses that preserve differential privacy.
Cite
@article{arxiv.1604.05590,
title = {Locating a Small Cluster Privately},
author = {Kobbi Nissim and Uri Stemmer and Salil Vadhan},
journal= {arXiv preprint arXiv:1604.05590},
year = {2017}
}