ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

FRAPP: a framework for high-accuracy privacy-preserving mining

Agrawal, Shipra and Haritsa, Jayant R and Prakash, B Aditya (2009) FRAPP: a framework for high-accuracy privacy-preserving mining. In: Data Mining and knowledge Discovery, 18 (1). pp. 101-139.

[img] PDF
fulltext.pdf - Published Version
Restricted to Registered users only

Download (723kB) | Request a copy
Official URL: http://www.springerlink.com/content/007xw560kgl8jl...


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.

Item Type: Journal Article
Publication: Data Mining and knowledge Discovery
Publisher: Springer
Additional Information: Copyright of this article belongs to Springer.
Keywords: Privacy;Data mining.
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 06 Nov 2009 04:11
Last Modified: 19 Sep 2010 05:00
URI: http://eprints.iisc.ac.in/id/eprint/18181

Actions (login required)

View Item View Item