Data Partition Based Encryption for Cloud Data Storage
B. Muthulakshmi1, M. Venkatesulu2
1B.Muthulakshmi, Department of Computer Applications, Kalasalingam University, Krishnan oil, (Tamil Nadu), India.
2M.Venkatesulu, Department of Computer Applications, Kalasalingam University, Krishnan oil, (Tamil Nadu), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1410-1416 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6930068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data security gains importance in cloud computing technology which is one of the most beneficial ubiquitous computing. It delivers resources and services via internet by Cloud Service Providers at cloud user’s choice of place. Data needs to be outsourced to avail this technology. Outsourced data to cloud is subjected to risks. Hence, researchers developed a lot of security techniques such as encryption schemes, authentication techniques to protect data in cloud. In this paper, a novel Invertible Non-linear Function based Cryptographic System (INFCS) is proposed to make the cloud storage secure and protected. The INFCS model comprises of partitioning, encryption and decryption. In partitioning technique, the data of data holder is partitioned or split into number of fragments which are then encrypted using invertible non-linear function. Then they are stored in one or more cloud storage(s). The decryption is performed at the end user’s side by doing inverse of invertible non-liner function. Use of the proposed INFCS makes the data more secure and preserved from any unauthorized users and malicious activities. The proposed INFCS is efficient and faster than other existing cryptographic systems.
Keyword: Cloud computing, Data security, Data partitioning, Pseudo plaintext, Invertible nonlinear function and Prime numbers.
Scope of the Article: Big Data Networking.