Siran Yin1,2, Leiming Yan1,2,*, Yuanmin Shi1,2, Yaoyang Hou1,2, Yunhong Zhang1,2
Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 175-185, 2020, DOI:10.32604/jihpp.2020.010780
Abstract Deep learning based on neural networks has made new progress in a
wide variety of domain, however, it is lack of protection for sensitive
information. The large amount of data used for training is easy to cause leakage
of private information, thus the attacker can easily restore input through the
representation of latent natural language. The privacy preserving deep learning
aims to solve the above problems. In this paper, first, we introduce how to reduce
training samples in order to reduce the amount of sensitive information, and then
describe how to unbiasedly represent the data with respect to specific attributes,… More >
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