Enhanced Security in Cloud Computing Using Neural Network and Encryption

doi:10.1109/ACCESS.2021.3122938

2021-01-01

Muhammad Usman Sana *

DOI：10.1109/ACCESS.2021.3122938

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摘要

En 中文

With the fast advancement in cloud computing, progressively more users store their applications and data on the cloud. Cloud computing has lots of features, e.g. virtualization, multi-user, efficiency, cost savings, and most importantly security. Machine learning approaches based on neural networks are being widely applied in cloud infrastructure when training is performed however this may produce possible privacy and security risk as direct access to raw data is required. To address this problem, we propose a new security design using Artificial Neural Networks (ANN) and encryption to confirm a safe communication system in the cloud environment, by letting the third parties access the data in an encrypted form for processing without disclosing the data of the provider party to secure important information. In this paper, to train neural networks using encrypted data we considered the Matrix Operation-based Randomization and Encipherment (MORE) technique, based on Fully Homomorphic Encryption (FHE). This technique allows the computations to be performed directly on floating-point data within a neural network with a minor computational overhead. 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 offers improved accuracy, runtime, and performance. These results highlight the potential of the proposed method to protect privacy and provide high accuracy in a reasonable amount of time when compared to other state-of-the-art techniques.

Keyword:

Cloud computing

Computational modeling

Data privacy

Biological neural networks

Privacy

Data models

Tools

Ciphertext

cloud computing

homomorphic encryption

matrix operation-based randomization and encipherment