DocumentCode :
568425
Title :
Secure Collaborative Outsourced Data Mining with Multi-owner in Cloud Computing
Author :
Lu, Qiwei ; Xiong, Yan ; Gong, Xudong ; Huang, Wenchao
Author_Institution :
Dept. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2012
fDate :
25-27 June 2012
Firstpage :
100
Lastpage :
108
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.
Keywords :
cloud computing; data mining; data privacy; groupware; outsourcing; security of data; support vector machines; trusted computing; K-means methods; KNN methods; SVM methods; cloud computing; computation resources; data partition; information society; malicious cloud assumption; multiple data owners; practical outsourced collaborative data mining scheme; privacy issues; secure collaborative outsourced data mining; semihonest cloud assumption; trusted computing technology; Authentication; Cloud computing; Collaboration; Data mining; Servers; Support vector machines; Cloud computing; Collaborative data mining; Outsourced data mining; Privacy-preserving; Trusted computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4673-2172-3
Type :
conf
DOI :
10.1109/TrustCom.2012.251
Filename :
6295964
Link To Document :
بازگشت