PPK-Means: Achieving Privacy-Preserving Clustering Over Encrypted Multi-Dimensional Cloud Data

Abstract

Clustering is a fundamental and critical data mining branch that has been widely used in practical applications such as user purchase model analysis, image color segmentation, outlier detection, and so on. With the increasing popularity of cloud computing, more and more encrypted data are converging to cloud computing platforms for enjoying the revolutionary advantages of the cloud computing paradigm, as well as mitigating the deeply concerned data privacy issues. However, traditional data encryption makes existing clustering schemes no more effective, which greatly obstructs effective data utilization and frustrates the wide adoption of cloud computing. In this paper, we focus on solving the clustering problem over encrypted cloud data. In particular, we propose a privacy-preserving k-means clustering technology over encrypted multi-dimensional cloud data by leveraging the scalar-product-preserving encryption primitive, called PPK-means. The proposed technique is able to achieve efficient multi-dimensional data clustering as well to preserve the confidentiality of the outsourced cloud data. To the best of our knowledge, our work is the first to explore the privacy-preserving multi-dimensional data clustering in the cloud computing environment. Extensive experiments in simulation data-sets and real-life data-sets demonstrate that our proposed PPK-means is secure, efficient, and practical.