Improved privacy-preserving classification method using non-negative matrix factorization
Guan Li · Computer Engineering and Applications Journal · 2015
In existing privacy-preserving data mining method based on NMF(Non-Negative Matrix Factorization), every sample is equally important, and is perturbed with same degree. For solving this problem, a new method using sample selection and based on NMF is proposed. This method uses sample selection to divide samples into two parts: the important samples and the unimportant samples. Both important and unimportant samples are perturbed by using the existing NMFbased method. And then, unimportant samples are perturbed additionally by using the clustering properties of the NMF.The experiments show that, when keeping data utility, this new method can protect privacy well.