A Novel Anti-classification Approach for Knowledge Protection.

ABSTRACT

Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached.