Voice-Authentication Model Based on Deep Learning for Cloud Environment - Hachim, Ethar Abdul Wahhab; Gaata, Methaq Talib; Abbas, Thekra (Society of Visual Informatics, 2023)
Cloud computing is becoming an essential technology for many organizations that are dynamically scalable and employ virtualized resources as a service done over the Internet. The security and privacy of the data stored in the cloud is cloud providers' main target. Every person wants to keep his data safe and store it in a secure place. The user considers cloud storage the best option to keep his data confidential without losing it. Authentication in the trusted cloud environment allows making knowledgeable authorization decisions for access to the protected individual's data. Voice authentication, also known as voice biometrics, depends on an individual's unique voice patterns for identification to access personal and sensitive data. The essential principle for voice authentication is that every person's voice differs in tone, pitch, and volume, which is adequate to make it uniquely distinguishable. This paper uses voice metric as an identifier to determine the authorized customers that can access the data in a cloud environment without risk. The Convolution Neural Network (CNN) architecture is proposed for identifying and classifying authorized and unauthorized people based on voice features. In addition, the 3DES algorithm is used to protect the voice features during the transfer between the client and cloud sides. In the testing, the experimental results of the proposed model achieve a high level of accuracy, reaching about 98%, and encryption efficiency metrics prove the proposed model's robustness against intended attacks to obtain the data.