A Multi-Privacy Level Encryption Framework for Secure Cloud-Based Data Repositories

The increasing reliance on cloud computing for storing and managing sensitive information has intensified concerns regarding data security. Traditional encryption methods, such as AES, though widely used, face limitations due to the evolving sophistication of cyber threats and their dependence on centralized access control systems, which introduce single points of failure. To address these challenges, this study proposes a robust, multi-layered encryption framework tailored for cloud environments. The approach integrates DNA-based encoding, AES encryption, SHA-256 hashing, and block-level file segmentation to ensure comprehensive data protection. DNA encoding introduces a complex transformation layer, AES guarantees data confidentiality, SHA-256 ensures integrity, and block segmentation enables efficient parallel processing and granular control. A key innovation of this framework is its decentralized key management system, which distributes control among multiple authorities. This decentralization eliminates the vulnerabilities of centralized schemes, enhances scalability, and avoids key escrow risks. The synergy of these mechanisms offers heightened resilience against unauthorized access while maintaining operational efficiency in multi-user cloud settings. The proposed framework significantly improves data confidentiality, integrity, and availability, laying a strong foundation for future advancements in secure cloud data storage and access control. It presents a scalable and practical solution to the limitations of existing cloud security models.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-15
DOI
https://doi.org/10.5281/zenodo.15666308
Primary Topic
Cloud Data Security Solutions
Type
article
Field-Weighted Citation Impact
0.00
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article

A Multi-Privacy Level Encryption Framework for Secure Cloud-Based Data Repositories

Zenodo (CERN European Organization for Nuclear Research)
Cloud Data Security Solutions
article

A Multi-Privacy Level Encryption Framework for Secure Cloud-Based Data Repositories

article en

Abstract

The increasing reliance on cloud computing for storing and managing sensitive information has intensified concerns regarding data security. Traditional encryption methods, such as AES, though widely used, face limitations due to the evolving sophistication of cyber threats and their dependence on centralized access control systems, which introduce single points of failure. To address these challenges, this study proposes a robust, multi-layered encryption framework tailored for cloud environments. The approach integrates DNA-based encoding, AES encryption, SHA-256 hashing, and block-level file segmentation to ensure comprehensive data protection. DNA encoding introduces a complex transformation layer, AES guarantees data confidentiality, SHA-256 ensures integrity, and block segmentation enables efficient parallel processing and granular control. A key innovation of this framework is its decentralized key management system, which distributes control among multiple authorities. This decentralization eliminates the vulnerabilities of centralized schemes, enhances scalability, and avoids key escrow risks. The synergy of these mechanisms offers heightened resilience against unauthorized access while maintaining operational efficiency in multi-user cloud settings. The proposed framework significantly improves data confidentiality, integrity, and availability, laying a strong foundation for future advancements in secure cloud data storage and access control. It presents a scalable and practical solution to the limitations of existing cloud security models.

Zenodo (CERN European Organization for Nuclear Research)
Periyar Maniammai Institute of Science & Technology (IN)
Openalex Percentile: Top 45%
Cloud Data Security Solutions
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A Multi-Privacy Level Encryption Framework for Secure Cloud-Based Data Repositories · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS