An Encrypted Neural Network Learning to Build Safe Trained Model
DOI:
https://doi.org/10.26438/ijcse/v6si1.3236Keywords:
Homomorphic encryption, neural networkAbstract
Neural network learning is a technique that is used to solve problems of classification, prediction, clustering, modelling based on variety of data inputs in the form of structured, semi-structured and unstructured data. Learning accuracy is considered as key performance index in these neural network based learning algorithms. Many organization that involves huge amount of data would want to outsource it to cloud for artificial intelligence based services. Various organization who wish to train neural network model on their complex and huge data usually outsource the learning model on cloud. Outsourcing of learning model on cloud creates security concerns for input data and the learned model. In this paper, we propose a practical system that will train a neural network model that is encrypted during training process. The training is performed on the unencrypted data. The output of the system is a neural network model that possesses two properties. First, neural network model is protected from the malicious users, hence allows the users to train the model in insecure environments at no cost of risk. Second, the neural network model can make only encrypted predictions. We make use of homomorphic encryption techniques to fulfill the objectives and test our results on sentiment analysis dataset.
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