Privacy-preserving logistic regression outsourcing in cloud computing

Authors:
Xu Dong Zhu;Hui Li;Feng Hua Li
Affiliations:
School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China;School of Telecommunication Engineering, Xidian University, Xi'an, Shaanxi 710071, China;State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
Venue:
International Journal of Grid and Utility Computing
Year:
2013

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Abstract

Cloud computing enables customers with limited computational resources an economically promising paradigm of computation outsourcing. However, how to protect customers' confidential data that is processed and generated during the computation is becoming a major security concern. To mitigate this problem, in this paper, we present a secure outsourcing mechanism for training and evaluating large-scale logistic regression classifier in cloud. Our mechanism enables a customer to securely harness the cloud, while keeping both the sensitive input and output of the computation private. Thorough security analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of our proposed design.