Privacy Preserving Classification Algorithm Based on Random Multidimensional Scales

A privacy preserving classification algorithm based on random Multidimensional Scales (MDS) is presented in this paper. We first alter the selection of the parameter embedded dimension d for satisfying the security of privacy preserving classification. Further the sensitive attributes are embedded into random (even higher) dimension feature space using random MDS algorithm, thus the sensitive attributes are transformed and protected. Because the transformed space dimension d is stochastic, this algorithm is not easily be breached. In addition, MDS can keep Euclidean distance of points, so the classification precision after encryption are kept well. The experiment shows that the present method can provide sensitive information enough protection without loss of the classification precision.