Privacy-Preserving Text Classification on Deep Neural Network

Abstract

With the explosive growth of Internet information, the classification of massive Internet data plays a very important role in real life. Text classification has been widely used in spam text recognition, intention recognition, text matching, named entity recognition, and other fields. At present, many enterprises provide APIs for text classification for users. Users can upload their data to the cloud server deployed by service providers for analysis, and return the final classification results. However, there is a risk of user data and model leakage in this process. To solve this problem, we propose a privacy-preserving text classification scheme using CKKS fully homomorphic encryption scheme and self-attention mechanism model in the multi-party security computing scenario. Our scheme ensures that user can achieve efficient encrypted data analysis under the premise of their data security, and user must be authorized by the service provider to use the model. Finally, compared with the experimental results of the previous research on privacy text classification under fully homomorphic encryption, the implementation improves the accuracy by 7.97% at most and speed-ups 282.4 times for inference at most, and we ensure the security of the protocol participants.