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-preserving mining in general, is evaluated specifically with regard to frequent-itemset mining on a variety of real datasets. Our experimental results indicate that, for a given privacy requirement, substantially lower errors are incurred, with respect to both itemset identity and itemset support, as compared to the prior techniques.