Privacy Preserving Machine Learning as a Service

• Main Author: Hesamifard, Ehsan
• Other Authors: Buckles, Bill
• Format: Others
• Language: English
• Published: University of North Texas 2020
• Subjects: Homomorphic encryption; machine learning; data privacy; deep learning; convolutional neural network; neural network
• Online Access: https://digital.library.unt.edu/ark:/67531/metadc1703277/

Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.