B-CNN: Betadeep Convolutional Neural Network over encrypted data

The results of automatic learning algorithms based on deep neural networks are impressive, and they are extensively used in a variety of fields. However, access to private information, frequently sensitive to confidentiality (financial, medical, etc.), is required in order to use them. This calls for good precision as well as special attention to the privacy and security of the data. In this paper, we propose a novel approach to solve this issue by using Convolutional Neural Network (CNN) model over encrypted data. In order to achieve our contribution, we focus on approximating the often used activation functions that seem to be the key functions in CNN networks which are: ReLu, Sigmoid and Tanh. We start by creating a low-degree polynomial, which is essential for a successful homomorphic encryption (HE). This polynomial which is based on Beta function and its primitive will be used as an activation function. The next step is to build a CNN model using batch normalization to ensure that the data are contained inside a limited interval. Finally, MNIST is used in order to evaluate our methodology and assess the effectiveness of the proposed approach. The experimental results support the efficacy of the proposed approach.