A Framework for High-Accuracy Privacy-Preserving Mining

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A Framework for High-Accuracy Privacy-Preserving Mining
To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a) the prior techniques differ only in their choices for the perturbation matrix elements, and (b) a symmetric perturbation matrix with minimal condition number can be identified, maximizing the accuracy even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the matrix elements are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at only a marginal cost in accuracy. The quantitative utility of FRAPP, which applies to random-perturbation-based privacy-preserv...
Shipra Agrawal, Jayant R. Haritsa
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2005
Where ICDE
Authors Shipra Agrawal, Jayant R. Haritsa
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