FRAPP: 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 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.
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References in zbMATH (referenced in 3 articles )
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- Chai, Jichong; Nayak, Tapan K.: A criterion for privacy protection in data collection and its attainment via randomized response procedures (2018)
- Cuzzocrea, Alfredo; Bertino, Elisa: Privacy preserving OLAP over distributed XML data: A theoretically-sound secure-multiparty-computation approach (2011)
- Agrawal, Shipra; Haritsa, Jayant R.; Prakash, B. Aditya: FRAPP: a framework for high-accuracy privacy-preserving mining (2009) ioport