A Dependable and Scalable Encryption Framework for Cloud Data Protection and Storage Optimization

Abstract

This research presents a novel secure and scalable encryption framework designed to address the growing challenges of cloud data protection and storage optimization. With the increasing reliance on cloud computing for data storage and management, ensuring the confidentiality, integrity, and availability of sensitive data is paramount. The proposed framework leverages advanced encryption techniques, such as homomorphic encryption and attribute-based encryption, to protect cloud data while maintaining efficient access control and minimizing performance overhead. The scalability of the framework ensures its applicability across diverse cloud environments, accommodating varying workloads and data volumes without compromising security. In addition to its strong security measures, the framework introduces innovative storage optimization strategies that reduce data redundancy and enhance resource utilization. By employing techniques like data deduplication and compression, the framework optimizes the storage capacity of cloud systems, making it both cost-effective and efficient. The research evaluates the performance of the proposed framework through rigorous simulations and real-world use cases, demonstrating its ability to deliver secure, scalable, and optimized cloud storage solutions. The results highlight the framework's potential to significantly improve data protection and operational efficiency in cloud environments.