# PrivFT: Private and Fast Text Classification with Homomorphic Encryption

arXiv: https://arxiv.org/abs/1908.06972 · HTML: https://mlsanity.com/paper/1908.06972/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [Encryption-Friendly LLM Architecture](https://mlsanity.com/paper/2410.02486/) (2024)
- [How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing](https://mlsanity.com/paper/2205.10095/) (2022)
- [HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption](https://mlsanity.com/paper/2403.14111/) (2024)
- [Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption](https://mlsanity.com/paper/2210.02574/) (2022)
- [SoK: Privacy-preserving Deep Learning with Homomorphic Encryption](https://mlsanity.com/paper/2112.12855/) (2021)
- [Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation](https://mlsanity.com/paper/2101.07365/) (2021)
- [SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges](https://mlsanity.com/paper/2204.05136/) (2022)
- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [User-Entity Differential Privacy in Learning Natural Language Models](https://mlsanity.com/paper/2211.01141/) (2022)
- [Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption](https://mlsanity.com/paper/2403.11166/) (2024)
- [ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping](https://mlsanity.com/paper/2506.19693/) (2025)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

The need for privacy-preserving analytics is higher than ever due to the severity of privacy risks and to comply with new privacy regulations leading to an amplified interest in privacy-preserving techniques that try to balance between privacy and utility. In this work, we present an efficient method for Text Classification while preserving the privacy of the content using Fully Homomorphic Encryption (FHE). Our system (named Private Fast Text (PrivFT)) performs two tasks: 1) making inference of encrypted user inputs using a plaintext model and 2) training an effective model using an encrypted dataset. For inference, we train a supervised model and outline a system for homomorphic inference on encrypted user inputs with zero loss to prediction accuracy. In the second part, we show how to train a model using fully encrypted data to generate an encrypted model. We provide a GPU implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme and compare it with existing CPU implementations to achieve 1 to 2 orders of magnitude speedup at various parameter settings. We implement PrivFT in GPUs to achieve a run time per inference of less than 0.66 seconds. Training on a relatively large encrypted dataset is more computationally intensive requiring 5.04 days.