A Blockchain-Based Security Framework for Privacy-Preserving Data Management in Cloud Computing
Keywords:
Blockchain, Cloud Computing, Data Privacy, Data Security, Smart Contracts, Access Control, Encryption, Cloud Storage.Abstract
Cloud computing provides flexible and scalable services for storing, processing, and sharing large amounts of data. Organizations increasingly use cloud platforms because they reduce infrastructure costs and provide convenient access to computing resources. However, storing sensitive information in centralized cloud environments creates several security and privacy challenges. Unauthorized access, data modification, insider threats, weak access control, and limited transparency are major concerns in cloud data management. Traditional security mechanisms such as authentication and encryption provide protection, but they still depend heavily on centralized cloud service providers. This paper proposes a Blockchain-Based Security Framework for Privacy-Preserving Data Management in Cloud Computing. The proposed framework combines blockchain, encryption, smart contracts, secure cloud storage, and access-control mechanisms. Sensitive data are encrypted before being stored in the cloud, while blockchain maintains important metadata, integrity information, and access records. Smart contracts automatically verify user permissions and control access to cloud resources. The framework also provides a tamper-resistant audit trail for monitoring data-management activities. By separating actual data storage from blockchain-based security records, the proposed approach avoids storing large files directly on the blockchain. The framework aims to improve data confidentiality, integrity, privacy, accountability, and access transparency in cloud computing environments.
References
1. Todupunuri, A. (2025). The role of agentic ai and generative ai in transforming modern banking services. American Journal of AI Cyber Computing Management, 5(3), 85-93.
2. Todupunuri, A. (2024). Develop machine learning models to predict customer lifetime value for banking customers, helping banks optimize services. International Journal of Advanced Research and Interdisciplinary Scientific Endeavours, 1(5), 275-282.
3. Prodduturi, S. M. K. (2024). Investigating the challenges and opportunities of cybersecurity in the era of remote work. European Journal of Advances in Engineering and Technology, 11(10), 80-84.
4. Prodduturi, S. M. K. (2024). Legal challenges in regulating AI-powered cybersecurity tools. International Journal of Engineering & Science Research, 14(4), 316-323.
5. Prodduturi, S. M. K. (2023). Leveraging Big Data And Business Intelligence To Revolutionise Corporate Strategy. International Journal for Research Trends and Innovation, 8(7).
6. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6), 463-474.
7. Bhagwat, V. B. (2025). Simplifying Payroll Balance Conversions in Payroll Systems Implementation through the Use of Generative AI.
9. Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.
10. Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(06), 1-8.
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