Privacy preserving method based on GM(1,1) and its application to data clustering

Protecting the users´ privacy while mining information from massive data has become a popular research topic in recent years. Perturbation and reconstruction are two common technologies in implementing privacy preserving data mining. In this paper, a novel perturbation method based on GM(1,1) model is proposed and applied to data clustering. The effectiveness and efficiency of the proposed method is demonstrated by the experiments on real-world datasets.