PrivFT: Private and Fast Text Classification with Homomorphic Encryption

TL;DR

PrivFT introduces an efficient GPU-based homomorphic encryption system for private text classification, enabling inference and training on encrypted data with minimal accuracy loss and significantly improved speed.

Contribution

It presents a novel GPU-accelerated homomorphic encryption framework for privacy-preserving text classification, including both inference and training on encrypted datasets.

Findings

GPU implementation achieves 10-100x speedup over CPU.

Inference runs in less than 0.66 seconds per input.

Training on encrypted data takes approximately 5 days.

Abstract

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 \textbf{Priv}ate \textbf{F}ast \textbf{T}ext (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.