@Article{jihpp.2020.010780,
AUTHOR = {Siran Yin, Leiming Yan, Yuanmin Shi, Yaoyang Hou, Yunhong Zhang},
TITLE = {A Survey on Recent Advances in Privacy Preserving Deep Learning},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {175--185},
URL = {http://www.techscience.com/jihpp/v2n4/41149},
ISSN = {2637-4226},
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, 
clarify the research results of other directions of privacy protection and its 
corresponding algorithms, summarize the common thoughts and existing 
problems. Finally, the commonly used datasets in the privacy protection research 
are discussed in this paper.},
DOI = {10.32604/jihpp.2020.010780}
}