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Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography

N. Niyaz Ahamed*, N. Duraipandian

Department of Computer Science and Engineering, Velammal Engineering College, Chennai, 600066, India

* Corresponding Author: N. Niyaz Ahamed. Email: email

Computer Systems Science and Engineering 2022, 41(1), 83-94. https://doi.org/10.32604/csse.2022.020071

Abstract

The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billion-dollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's interior. So, we suggest a model called multi-layer storage (MLS) based on security using elliptical curve cryptography (ECC). The suggested model focuses on the significance of cloud storage along with data protection and removing duplicates at the initial level. Based on divide and combine methodologies, the data are divided into three parts. Here, the first two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding technique. The other part of the encrypted data is saved in the cloud. The viability of our model has been tested by research in terms of safety measures and test evaluation, and it is truly a powerful complement to existing methods in cloud storage.

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Cite This Article

N. Niyaz Ahamed and N. Duraipandian, "Secured data storage using deduplication in cloud computing based on elliptic curve cryptography," Computer Systems Science and Engineering, vol. 41, no.1, pp. 83–94, 2022. https://doi.org/10.32604/csse.2022.020071



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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