ECDS: Enhanced Cloud Data Security Technique to Protect Data Being Stored in Cloud Infrastructure
Data Security in Cloud Infrastructure
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.4.19Keywords:
Cloud computing security, Data protection, Cryptographic, Symmetric encryption, Data encryptionDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The rapid adoption of cloud computing has transformed IT resource management by providing scalable, flexible, and cost-effective solutions. Despite these benefits, cloud computing presents critical security challenges, particularly in protecting sensitive data during transmission and storage. This paper introduces the Enhanced Cloud Data Security (ECDS) technique, a new approach aimed at strengthening data protection within cloud infrastructures. ECDS incorporates substitution and permutation methods to secure data and utilizes a combination of encryption strategies to ensure that encrypted data remains inaccessible to unauthorized users. ECDS is a symmetric cryptographic system that uses the same key for encryption and decryption. It is 256-bit block cipher encryption and it uses 312-but keys. The ECDS is implemented in Python and compared against DES and Blowfish Encryption techniques. Extensive testing and performance analysis reveal that ECDS significantly enhances security and efficiency compared to traditional encryption methods. This paper contributes to the ongoing efforts to secure cloud computing environments for safeguarding sensitive data in the cloud.Abstract
How to Cite
Downloads
Similar Articles
- Mohamed Azharudheen A, Vijayalakshmi V, Improvement of data analysis and protection using novel privacy-preserving methods for big data application , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- D. Jayadurga, A. Chandrabose, Expanding the quantity of virtual machines utilized within an open-source cloud infrastructure , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Jayalakshmi K., M. Prabakaran, The role of big data in transforming human resource analytics: A literature review , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- A. Jabeen, AR Mohamed Shanavas, Bradley Terry Brownboost and Lemke flower pollinated resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Rekha R., P. Meenakshi Sundaram, Enhanced malicious node identification in WSNs with directed acyclic graphs and RC4-based encryption , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V. Baby Deepa, R. Jeya, Dynamic resource allocation with otpimization techniques for qos in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Lakshmi Priya, Anil Vasoya, C. Boopathi, Muthukumar Marappan, Evaluating dynamics, security, and performance metrics for smart manufacturing , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Archana G, Vijayalakshmi V, Improving classification precision for medical decision systems through big data analytics application , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Santhanalakshmi M, Ms Lakshana K, Ms Shahitya G M, Enhanced AES-256 cipher round algorithm for IoT applications , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- P. Hepsibah Kenneth, E. George Dharma Prakash Raj, Priority based parallel processing multi user multi task scheduling algorithm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

