# Training Natural Language Processing Models on Encrypted Text for Enhanced Privacy

arXiv: https://arxiv.org/abs/2305.03497 · HTML: https://mlsanity.com/paper/2305.03497/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 4 of 4)

- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [A review of homomorphic encryption and software tools for encrypted statistical machine learning](https://mlsanity.com/paper/1508.06574/) (2015)
- [Auto-tagging of Short Conversational Sentences using Transformer Methods](https://mlsanity.com/paper/2106.01735/) (2021)

## Abstract (arXiv)

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