Abstract | ||
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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 positive-definite perturbation matrix with minimal condition number can be identified, substantially enhancing the accuracy even under strict privacy requirements. 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 reduction in accuracy. The quantitative utility of FRAPP, which is a general-purpose random-perturbation-based privacy-preserving mining technique, is evaluated specifically with regard to association and classification rule mining on a variety of real datasets. Our experimental results indicate that, for a given privacy requirement, either substantially lower modeling errors are incurred as compared to the prior techniques, or the errors are comparable to those of direct mining on the true database. |
Year | DOI | Venue |
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2009 | 10.1007/s10618-008-0119-9 | Data Min. Knowl. Discov. |
Keywords | DocType | Volume |
Privacy,Data mining | Journal | 18 |
Issue | ISSN | Citations |
1 | 1384-5810 | 6 |
PageRank | References | Authors |
0.44 | 30 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Shipra Agrawal | 1 | 580 | 37.48 |
Jayant R. Haritsa | 2 | 2004 | 228.38 |
B. Aditya Prakash | 3 | 941 | 53.95 |