Abstract: We studied k-means privacy preserving clustering method within the framework of differential privacy. We first introduced the research status of privacy preserve data mining and privacy preserve clustering, briefly presenting the basic principle and method of differential privacy. To improve the poor clustering availability of differential privacy k-means, we presented a new method of IDP k-means clustering and proved it satisfies E-differential privacy. Our experimenu show that at the same level of privacy preserve, IDP k-means clustering gets a much higher clustering availability than differential privacy k-means clustering method.

Key words: Differential privacy,k-mcans,Clustering,Privacy preserving