Secure Collaborative Outsourced Data Mining with Multi-owner in Cloud Computing

Abstract

Data mining is an important technology for the information society. Due to the limited computation resources of data owners and the prevalence of cloud computing, outsourced data mining is becoming more and more attractive. The privacy and security issues are becoming outstanding recently. Though the existing model of cloud computing consists of multiple data owners, there is little consideration for the collaboration between them. But such collaboration is necessary with the trend of data partition among different entities nowadays. Besides, most of the existing work are based on the semi-honest cloud assumption and can not deal with the malicious cloud situation well. In this paper, we explore the secure and practical outsourced collaborative data mining scheme in cloud computing scenarios. We design a simple framework for it and propose several enhanced frameworks and detailed schemes in an incremental way with stronger security considerations. The final framework utilizes trusted computing technology to design the scheme under the malicious cloud assumption. Finally, we give a summary of security and efficiency analysis about them. As a case of study, we prove the correctness of the frameworks with three classical methods KNN, K-means and SVM respectively in such outsourced collaborative computing scenario.