Adaptive Artificial Bee Colony Algorithm-Based Enhancement of Data Security in Cloud Computing

• Published in: SN computer science Vol. 5; no. 1; p. 98
• Main Author: Geetha, J. Sai
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2024; Springer Nature B.V
• Subjects: Access control; Biometrics; Cloud computing; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Confidentiality; Cryptography; Curves; Cybersecurity; Data storage; Data Structures and Information Theory; Encryption; Fingers; Industrial IoT and Cyber-Physical Systems; Information Systems and Communication Service; Optimization algorithms; Original Research; Pattern Recognition and Graphics; Privacy; Resource utilization; Search algorithms; Servers; Software Engineering/Programming and Operating Systems; Swarm intelligence; User needs; Vision; Cloud computing; Confidentiality; Data storage; Data retrieval; Biometric authentication; Artificial bee colony algorithm; Cryptography
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-023-02419-3

Nowadays, the usage of network has been increased due to cloud computing. It is used in many applications because of its salient features such as reduced cost, hassle-free usage without the requirement of client–server model. The networked resources of cloud computing environment support the data storage in remote server. It has been used in more applications particularly in resource utilization and data sharing. The fundamental functions of security and user authentication must be improved for cloud computing to operate more efficiently. The above tasks should be achieved through the Artificial Bee Colony with split encryption process and biometric authentication. The biometric authentication is utilized to improve the user authentication in public cloud environment. In addition, the method of split encryption process in Artificial Bee Colony algorithm helps to improve the data security. The proposed methodology, namely biometric-based split encryption (BSE) accomplishes two main goals: (i) enhancement of security and (ii) user authentication. The split encryption process with ABC algorithm is applied to the sensitive data and also combined with the finger impression of the authenticated user. To prevent unwanted access to encrypted data, the resulting data and the biometric authentication should be kept distinct in the cloud storage area and verified before decrypting the original data. The execution time, which includes encryption and decryption, as well as memory usage, are used to gauge the effectiveness of the biometric-based split encryption (BSE). The results of BSE algorithm are compared with the emperor penguin optimization (EPO) with Elliptic curve cryptography.