FRAPP swMATH ID: 8861 Software Authors: Shipra Agrawal, Jayant R. Haritsa, B. Aditya Prakash Description: 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. Homepage: http://link.springer.com/article/10.1007/s10618-008-0119-9 Related Software: RAPPOR; XMark; C4.5 Cited in: 2 Documents Cited by 4 Authors 1 Bertino, Elisa 1 Chai, Jichong 1 Cuzzocrea, Alfredo 1 Nayak, Tapan Kumar Cited in 2 Serials 1 Journal of Computer and System Sciences 1 Electronic Journal of Statistics Cited in 2 Fields 1 Statistics (62-XX) 1 Computer science (68-XX) Citations by Year