Privacy-preserving logistic regression outsourcing in cloud computing. (1st January 2013) - Zhu, Xu Dong; Li, Hui; Li, Feng Hua
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.