Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Towards accurate histogram publication under differential privacy

Language

English

Abstract

Histograms arc the workhorse of data mining and analysis. This paper considers the problem of publishing histograms under differential privacy, one of the strongest privacy models. Existing differentially private histogram publication schemes have shown that clustering (or grouping) is a promising idea to improve the accuracy of sanitized histograms. However, none of them fully exploits the benefit of clustering. In this paper, we introduce a new clustering framework. It features a sophisticated evaluation of the trade-off between the approximation error due to clustering and the Laplace error due to Laplace noise injected, which is normally overlooked in prior work. In particular, we propose three clustering strategies with different orders of run-time complexitics. We prove the superiority of our approach by theoretical utility comparisons with the competitors. Our extensive experiments over various standard real-life and synthetic datasets confirm that our technique consistently outperforms existing competitors.

Publication Date

2014

Source Publication Title

Proceedings of the 2014 SIAM International Conference on Data Mining

Start Page

587

End Page

595

Conference Location

Philadelphia, United States

Publisher

SIAM

DOI

10.1137/1.9781611973440.68

Link to Publisher's Edition

http://dx.doi.org/10.1137/1.9781611973440.68

ISBN (electronic)

9781611973440

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