Deep Learning as a Service Based on Encrypted Data

Abstract

With the widespread promotion of 4G/5G wireless network and the rapid development of Internet of Things (IoT), more and more enterprises begin to make use of their powerful computing power and technology to provide pre-trained deep neural networks for ordinary users to help them complete classification, regression, image recognition, NLP and other services. This not only brings convenience to people, but also easily causes the leakage of users' private data. In this paper, we combine deep learning with homomorphic encryption algorithm and design a deep learning network model based on secure Multi-party computing (MPC). In the whole process, we realize that the cloud only owns the encryption samples of users, and users do not own any parameters or structural information related to the model. In the experimental part, we input the encrypted Mnist and Cifar-l0 datasets into the model for testing, and the results show that the classification accuracy rate of the encrypted Mnist can reach 99.21%, which is very close to the result under plaintext. The classification accuracy rate of encrypted cifar-l0 can reach 91.35%, slightly lower than the test result in plaintext, and better than the existing deep learning network model that can realize data privacy protection.