Training Natural Language Processing Models on Encrypted Text for Enhanced Privacy
This addresses privacy concerns for users of NLP services handling sensitive data, but it is incremental as it builds on existing encryption and model techniques.
The paper tackles the problem of data privacy in cloud-based NLP model training by proposing a method to train models on encrypted text, achieving comparable performance to non-encrypted models on the 20 Newsgroups dataset.
With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP