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
- G. Vijayalakshmi, M. V. Srinath, Student’s Academic Performance Improvement Using Adaptive Ensemble Learning Method , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- L. Amudavalli, K. Muthuramalingam, Energy-efficient location-based routing protocol for wireless sensor networks using teaching-learning soccer league optimization (TLSLO) , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Naveena Somasundaram, Vigneshkumar M, Sanjay R. Pawar, M. Amutha, Balu S, Priya V, AI-driven material design for tissue engineering a comprehensive approach integrating generative adversarial networks and high-throughput experimentation , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Vimala S, G. Arockia Sahaya Sheela, Label-Aware Imputation with Cluster Refinement for Smartphone Usage Analytics in Educational Institutions , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- C. Muruganandam, V. Maniraj, A Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S. Dhivya, S. Prakash, Power quality assessment in solar-connected smart grids via hybrid attention-residual network for power quality (HARN-PQ) , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Olivia C. Gold, Jayasimman Lawrence, Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Ritu Nagila, Abhishek Kumar Mishra, Ashish Nagila, Role of big data in enhancing lung cancer prediction and treatment , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Swetha Rajkumar, Jayaprasanth Devakumar, LSTM based data driven fault detection and isolation in small modular reactors , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- R. Kalaiselvi, P. Meenakshi Sundaram, Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
<< < 6 7 8 9 10 11 12 13 14 15 > >>
You may also start an advanced similarity search for this article.

