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Improved Cloud Storage Encryption Using Block Cipher-Based DNA Anti-Codify Model

E. Srimathi1,*, S. P. Chokkalingam2

1 SRM Institute of Science and Technology, Ramapuram Campus, Chennai, 600089, Tamilnadu, India
2 Department of CSE, Saveetha Institute of Medical and Technical Sciences, 602105, Tamilnadu, India

* Corresponding Author: E. Srimathi. Email: email

Computer Systems Science and Engineering 2023, 47(1), 903-918. https://doi.org/10.32604/csse.2023.029790

Abstract

When it comes to data storage, cloud computing and cloud storage providers play a critical role. The cloud data can be accessed from any location with an internet connection. Additionally, the risk of losing privacy when data is stored in a cloud environment is also increased. A variety of security techniques are employed in the cloud to enhance security. In this paper, we aim at maintaining the privacy of stored data in cloud environment by implementing block-based modelling to boost the privacy level with Anti-Codify Technique (ACoT) and block cipher-based algorithms. Initially, the cipher text is generated using Deoxyribo Nucleic Acid (DNA) model. Block-cipher-based encryption is used by ACoT, but the original encrypted file and its extension are broken up into separate blocks. When the original file is broken up into two separate blocks, it raises the security level and makes it more difficult for outsiders to cloud data access. ACoT improves the security and privacy of cloud storage data. Finally, the fuzzy-based classification is used that stores various access types in servers. The simulation results shows that the ACoT-DNA method achieves higher entropy against various block size with reduced computational cost than existing methods.

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Cite This Article

E. Srimathi and S. P. Chokkalingam, "Improved cloud storage encryption using block cipher-based dna anti-codify model," Computer Systems Science and Engineering, vol. 47, no.1, pp. 903–918, 2023. https://doi.org/10.32604/csse.2023.029790



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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