Privacy-preserving deep learning on big data in cloud
Yongkai Fan, Wanyu Zhang, Jianrong Bai, Xia Lei, Kuan‐Ching Li
Abstract
In the analysis of big data, deep learning is a crucial technique. Big data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage areas. Nevertheless, there is a contradiction between the open nature of the cloud and the demand that data owners maintain their privacy. To use cloud resources for privacy-preserving data training, a viable method must be found. A privacy-preserving deep learning model (PPDLM) is suggested in this research to address this preserving issue. To preserve data privacy, we first encrypted the data using homomorphic encryption (HE) approach. Moreover, the deep learning algorithm's activation function—the sigmoid function—uses the least-squares method to process nonaddition and non-multiplication operations that are not allowed by homomorphic. Finally, experimental results show that PPDLM has a significant effect on the protection of data privacy information. Compared with Non-Privacy Preserving Deep Learning Model (NPPDLM), PPDLM has higher computational efficiency.