Deriving Private Information from General Linear Transfor mation Perturbed Data

Several perturbation techniques have been proposed for privacy preserving data mining, among which the most popular ones are the additive noise based randomization approach and the rotation based perturbation approach. This paper first presents a general framework based on the general linear transformation, which incorporates both the ad ditive noise based and the rotation based perturbation approaches. Then it explores its privacy preserving property . Specifically, an Independent Component Analysis based reconstruction method, AK-ICA, is proposed to breach privacy when a subset of sample data are a-priori known by attackers. The theoretical analysis and experimental resu lts show that all current transformed privacy preserving data mining techniques may need a careful scrutiny in order to prevent privacy breaches when a subset of sample data are available.

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