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
- S. Ramkumar, K. Aanandha Saravanan, Martin Joel Rathnam, M. Revathy, Integration of AI and agent-based modeling for simulating human-ecological systems , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- J. Helan Shali Margret, N. Amsaveni, Application of Lotka’s law in Indian cytokine publications: A scientometric study based on web of science during 1998 TO 2022 , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Vishakha Khambhati, Rajan Kumar Singh, Assessment of Respiratory Dynamics from ECG during Physical Exertion , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Navjot Singh, Sultan Singh, Demographic perception of customers towards dairy marketing practices: An empirical study , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- G Gayathri Devi, R Radha, Smart alerting services: Safeguarding women and children in the digital age , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Ramesh Babu Durai C, D. Madhivadhani, A. Sumathi, Lily Saron Grace, Graph neural networks for modeling ecological networks and food webs , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Merla Agnes Mary, Britto Ramesh Kumar, Hybrid GAN with KNN - SMOTE Approach for Class-Imbalance in Non-Invasive Fetal ECG Monitoring , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- UMASHANKAR SHUKLA, ANIL K. UPADHYAY, MATHEMATICAL MODEL FOR INFECTION AND REMOVAL IN POPULATION , The Scientific Temper: Vol. 10 No. 1&2 (2019): The Scientific Temper
- R. Sudha, B Indira, M Kalidas, Kalluri Rama Krishna, M. Jithender Reddy, G.N.R. Prasad, E-commerce in the B2B market: solutions for the point of sale , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 13 14 15 16 17 18 19 20 21 22 > >>
You may also start an advanced similarity search for this article.

