On Privacy-Preserving Histograms
Data Structures and Algorithms
2012-07-09 v1
Abstract
We advance the approach initiated by Chawla et al. for sanitizing (census) data so as to preserve the privacy of respondents while simultaneously extracting "useful" statistical information. First, we extend the scope of their techniques to a broad and rich class of distributions, specifically, mixtures of highdimensional balls, spheres, Gaussians, and other "nice" distributions. Second, we randomize the histogram constructions to preserve spatial characteristics of the data, allowing us to approximate various quantities of interest, e.g., cost of the minimum spanning tree on the data, in a privacy-preserving fashion.
Keywords
Cite
@article{arxiv.1207.1371,
title = {On Privacy-Preserving Histograms},
author = {Shuchi Chawla and Cynthia Dwork and Frank McSherry and Kunal Talwar},
journal= {arXiv preprint arXiv:1207.1371},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)