Application of Data Encryption in Chinese Named Entity Recognition

TL;DR

This paper introduces an encryption-based framework for Chinese Named Entity Recognition, enabling training on encrypted data to protect privacy without significantly compromising model performance.

Contribution

It is the first to apply multiple encryption algorithms to NER training data, demonstrating effective privacy preservation with maintained or improved accuracy.

Findings

Encryption methods achieve satisfactory NER performance.

Some models trained on encrypted data outperform unencrypted counterparts.

The approach helps mitigate data leakage in sensitive domains.

Abstract

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.