Enhanced Positional Vigenère (EPV): A Confidentiality-Enabled Encryption Technique for Secure Cloud Storage
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2026.17.4.09Keywords:
Cloud Computing, Data Security, Encryption, Enhanced Positional Vigenère (EPV)Dimensions Badge
Issue
Section
License
Copyright (c) 2026 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Cloud computing is an internet-based computing paradigm that provides various hosting and delivery services over the internet. It offers computational resources to users based on their demand. Data storage is one of the main benefits of cloud computing. It provides users with plenty of space to store their data neatly and access it easily, no fuss involved. More and more companies are jumping on cloud platforms that offer Storage as a Service (STaaS), helping them skip the hefty upfront costs and ongoing maintenance of their own servers. However, when organizational and enterprise data are moved to public cloud storage, ensuring data protection and security becomes a critical concern. If unencrypted data is transmitted to the public cloud, there is a possibility of data breaches during transmission. To address this issue, an efficient technique called Enhanced Positional Vigenère (EPV) is proposed in this work. Strengthening the ciphertext and improving data security are the goals of the suggested approach. The EPV technique is implemented in Java, and our experiments show it boosts performance, ramps up efficiency, and makes the ciphertext even more complex.Abstract
How to Cite
Downloads
Similar Articles
- M. Ragul, A. Aloysius, V. Arul Kumar, Enhancing IoT blockchain scalability through the eepos consensus algorithm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Vijay Sharma, Nishu, Anshu Malhotra, An encryption and decryption of phonetic alphabets using signed graphs , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- A. Jabeen, A. R. M. Shanavas, Hazard regressive multipoint elitist spiral search optimization for resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Dileep Pulugu, Shaik K. Ahamed, Senthil Vadivu, Nisarg Gandhewar, U D Prasan, S. Koteswari, Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S. C. Prabha, P. Sivaraaj, S. Kantha Lakshmi, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- S. Gomathi, C. Radhika, A secure messaging application using steganography and AES encryption a dual-layer secure messaging system , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Rahat Yezdani, S. M. K. Quadri, A PPR-based energy-efficient VM consolidation in cloud computing , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Kumari Neha, Amrita ., Quantum programming: Working with IBM’S qiskit tool , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): 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
<< < 3 4 5 6 7 8 9 10 11 12 > >>
You may also start an advanced similarity search for this article.

