FISIP: A Distance and Correlation Preserving Transformation for Privacy Preserving Data Mining

This paper devises a transformation scheme to protect data privacy in the case that data have to be sent to the third party for the analysis purpose. Most conventional transformation schemes suffer from two limits, i.e., the algorithm dependency and the information loss. In this work, we propose a novel privacy preserving transformation scheme without these two limitations. The transformation is referred to as FISIP. Explicitly, by preserving three basic properties, i.e., the first order sum, the second order sum and inner products, of the private data, mining algorithms which depend on these three properties can still be applied to public data. Specifically, any distance-based or correlation-based algorithm has the same performance on the transformed public data as on the original private data. Special perturbation can be added into FISIP transformations to increase the protection level. In the experimental results, FISIP attains data usefulness and data robustness at the same time. In summary, FISIP is able to provide a privacy preserving scheme that preserves the distance and the correlation of the private data after the transformation to the public data.