A Secure Data Concealment Framework for Privacy-Preserving Cloud Storage and Access
DOI:
https://doi.org/10.15662/IJEETR.2026.0802379Keywords:
Cloud Security, Data Concealment, Cloaking Wall Model, Cloud Storage Privacy, ChaCha20 Encryption, Chaffing and Winnowing, Access Pattern Protection, Secure Cloud ComputingAbstract
Cloud computing offers scalable and cost-efficient storage for modern organizations, but it also introduces serious challenges in protecting sensitive data stored on third-party platforms. Issues such as data privacy, unauthorized access, and weak access control mechanisms make traditional security methods insufficient. While conventional encryption techniques safeguard the content of data, they do not effectively conceal access patterns or contextual information like user location and access time, which can still be exploited by attackers.
To overcome these limitations, this research introduces a Secure Data Concealment Framework designed to enhance privacy in cloud environments. The framework is built on a Cloaking Wall Model, which works alongside Camouflage Data Disguise techniques to obscure both data and its usage patterns. It employs four key cloaking strategies: Long-Term Cloaking ensures prolonged protection of sensitive data, Multi-Region Cloaking distributes access control across different geographic zones, Time-Based Cloaking restricts access based on specific time conditions, and Geo-Location-Based Cloaking limits data visibility depending on user location. These strategies collectively enable context-aware and controlled data access.
Additionally, the framework integrates Chaffing and Winnowing with the ChaCha20 encryption algorithm to strengthen security. This approach generates misleading or fake data (chaff) for unauthorized users and bots, while legitimate users can filter out and access the real data (winnowing). Even if attackers gain entry, they are unable to distinguish genuine information from disguised data, significantly reducing the risk of data breaches and inference attacks.
Experimental results demonstrate that this approach enhances data confidentiality, improves access control mechanisms, and minimizes unauthorized data interpretation. Importantly, it achieves these security benefits with low computational overhead, making it practical for real-world, large-scale cloud storage systems.
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