Privacy protection is indispensable in data mining

and many PPDM (privacy-preserving data mining) methods have been proposed.One such method based on data perturbation is SVD (singular value decomposition)-based method

which treats all samples and attributes equally.However

different samples and attributes may have different requirements for privacy protection

and may be not equally important for data mining.So

it is better to treat them differently.This paper proposed an improved SVD-based perturbation method

which can perturb different samples and attributes to different degrees.In addition

this paper proposed an improved privacy-preserving classification mining method using this improved SVD-based perturbation algorithm.The experiments showed that while maintaining data utility

the proposed privacy-preserving classification mining method can protect privacy better than the original SVD-based method.