Uncertainty in Artificial Intelligence
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On Privacy-Preserving Histograms
Shuchi Chawla, Cynthia Dwork, Frank McSherry, Kunal Talwar
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:
Pages: 120-127
PS Link:
PDF Link: /papers/05/p120-chawla.pdf
BibTex:
@INPROCEEDINGS{Chawla05,
AUTHOR = "Shuchi Chawla and Cynthia Dwork and Frank McSherry and Kunal Talwar",
TITLE = "On Privacy-Preserving Histograms",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "120--127"
}


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