Distributed Large Scale Privacy-Preserving Deep Mining
Hao Xue, Zheng Huang, Huijuan Lian, Weidong Qiu, Jie Guo, Shen Wang, Zheng Hu Gong · 2018
With the increasing of the data quantity and the continuous development in data mining applications, the introduction of deep learning is imminent. However, the combination of data mining and deep learning still faces many challenges in distributed large-scale environment, and one of the most immediate issues is to ensure the privacy of data when running deep mining in cloud. In this paper, we propose a scheme combined the multi-key fully homomorphic encryption with the neural networks. With the scheme, participates protect their data with the fully homomorphic encryption which could achieve the requirements of preserving the privacy of distributed data in deep mining.