ML Anthology

On Privacy-Preserving Histograms

Shuchi Chawla, Cynthia Dwork, Frank McSherry, Kunal Talwar
UAI 2005
/uai/2005/chawla2005uai-privacy/

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.

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Text

Chawla et al. "On Privacy-Preserving Histograms." Conference on Uncertainty in Artificial Intelligence, 2005.

Markdown

[Chawla et al. "On Privacy-Preserving Histograms." Conference on Uncertainty in Artificial Intelligence, 2005.](https://mlanthology.org/uai/2005/chawla2005uai-privacy/)

BibTeX

@inproceedings{chawla2005uai-privacy,
  title     = {{On Privacy-Preserving Histograms}},
  author    = {Chawla, Shuchi and Dwork, Cynthia and McSherry, Frank and Talwar, Kunal},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2005},
  url       = {https://mlanthology.org/uai/2005/chawla2005uai-privacy/}
}