  Recently, with the continuous development of deep learning, the performance
of named entity recognition tasks has been dramatically improved. However, the
privacy and the confidentiality of data in some specific fields, such as
biomedical and military, cause insufficient data to support the training of
deep neural networks. In this paper, we propose an encryption learning
framework to address the problems of data leakage and inconvenient disclosure
of sensitive data in certain domains. We introduce multiple encryption
algorithms to encrypt training data in the named entity recognition task for
the first time. In other words, we train the deep neural network using the
encrypted data. We conduct experiments on six Chinese datasets, three of which
are constructed by ourselves. The experimental results show that the encryption
method achieves satisfactory results. The performance of some models trained
with encrypted data even exceeds the performance of the unencrypted method,
which verifies the effectiveness of the introduced encryption method and solves
the problem of data leakage to a certain extent.
