Enhanced Security in Cloud Computing Using Neural Network and Encryption

In the last five years, demand for cloud computing among businesses and individual users is increasing immensely because of numerous reasons including, improved productivity, efficiency and speed, cost savings, performance, and most importantly security.Machine learning techniques are making progress in a variety of domains of cloud computing to resolve security concerns and manage data efficiently.In cloud security, a relatively novel approach is Artificial Neural Networks (ANN).We propose a new security design using neural network and encryption to confirm a safe communication system in the cloud environment, by letting the third parties access the information in an encrypted form without disclosing the data of the provider party to secure important information.We recommend a solution based on fully homomorphic encryption (FHE) to handle sensitive information without revealing the original data.The encryption technique we considered is matrix operation-based randomization and encipherment (MORE), which allows the computations to be performed directly on floating-point data within a neural network with a minor computational overhead.In this paper, we examined the speech and voice recognition problem and the performance of the proposed method has been validated in MATLAB simulation.Results showed that applying neural network training with MORE improved accuracy, runtime, and performance.These results highlight the potential of the proposed neural network and encryption technique to protect the privacy and providing high accuracy in a reasonable period when compared to other state of the art techniques.