Badawi, A. A., Hoang, L., Mun, C. F., Laine, K., & Aung, K. M. M. (2020). PrivFT: Private and Fast Text Classification With Homomorphic Encryption. IEEE Access, 8, 226544–226556. doi:10.1109/access.2020.3045465
We present an efficient and non-interactive method for Text Classification while preserving the privacy of the content using Fully Homomorphic Encryption (FHE). Our solution (named Private Fast
Text (PrivFT)) provides two services: 1) making inference of encrypted user inputs using a plaintext model and 2) training an effective model using an encrypted dataset. For inference, we use a pre-trained plaintext model and outline a system for homomorphic inference on encrypted user inputs with zero loss to prediction accuracy compared to the non-encrypted version. In the second part, we show how to train a supervised model using fully encrypted data to generate an encrypted model. For improved performance, we provide a GPU
implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme that shows 1 to 2 orders of magnitude speedup against existing implementations. We build PrivFT on top of our FHE engine in GPUs to achieve a run time per inference of 0.17 seconds for various Natural Language Processing (NLP) public datasets. Training on a relatively large encrypted dataset is more computationally intensive requiring 5.04 days.
Attribution 4.0 International (CC BY 4.0)
This research / project is supported by the Agency for Science, Technology and Research - RIE2020 Advanced Manufacturing and Engineering (AME) Programmtic Programme
Grant Reference no. : A19E3b0099