Secure Framework Enhancing AES Algorithm in Cloud Computing

• Published in: Security and communication networks Vol. 2020; no. 2020; pp. 1 - 16
• Main Authors: Akhtar, Rizwan, Shaheen, Qaisar, Hashmi, Muhammad Usman, Shiraz, Muhammad, Awan, Ijaz Ahmad, Ditta, Allah
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Access control; Algorithms; Cloud computing; Data centers; Data encryption; Digital signatures; Encryption; Energy consumption; Genetic algorithms; Literature reviews; Power consumption; Privacy; Quality management; Resource management; Resource utilization; Social research; Software services
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2020/8863345

The tremendous growth of computational clouds has attracted and enabled intensive computation on resource-constrained client devices. Predominantly, smart mobiles are enabled to deploy data and computational intensive applications by leveraging on the demand service model of remote data centres. However, outsourcing personal and confidential data to the remote data servers is challenging for the reason of new issues involved in data privacy and security. Therefore, the traditional advanced encryption standard (AES) algorithm needs to be enhanced in order to cope with the emerging security threats in the cloud environment. This research presents a framework with key features including enhanced security and owner’s data privacy. It modifies the 128 AES algorithm to increase the speed of the encryption process, 1000 blocks per second, by the double round key feature. However, traditionally, there is a single round key with 800 blocks per second. The proposed algorithm involves less power consumption, better load balancing, and enhanced trust and resource management on the network. The proposed framework includes deployment of AES with 16, 32, 64, and 128 plain text bytes. Simulation results are visualized in a way that depicts suitability of the algorithm while achieving particular quality attributes. Results show that the proposed framework minimizes energy consumption by 14.43%, network usage by 11.53%, and delay by 15.67%. Hence, the proposed framework enhances security, minimizes resource utilization, and reduces delay while deploying services of computational clouds.