Ensemble Homomorphic Encrypted Data Classification

Dana R Alsagheer, Hadi Mansouifar, lei Xu, Qian Lou, Weidong Shi, lin chen

Keywords: Machine Learning Privacy, Homomorphic Encrypted Data Classification, Ensemble Learning

Abstract: Homomorphic encryption (HE) is encryption that permits users to perform computations on encrypted data without first decrypting it. HE can be used for privacy-preserving outsourced computation and analysis, allowing data to be encrypted and outsourced to commercial cloud environments for processing while encrypted or sensitive data. HE enables new services by removing privacy barriers inhibiting data sharing or increasing the security of existing services. A convolution neural network (CNN) with shallow architecture can be homomorphically evaluated using addition and multiplication by replacing the activation function, such as ReLU, with a low polynomial degree. To achieve the same performance as the ReLU activation function, we study the impact of applying the ensemble techniques to solve the accuracy problem. Our experimental results empirically show that the ensemble approach can reduce bias, and variance, increasing accuracy to achieve the same ReLU performance with parallel and sequential techniques. We demonstrate the effectiveness and robustness of our method using three data sets: MNIST, FMNIST, and CIFAR-10

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