Design and Development of Privacy-Preserving Frameworks for Secure Data Storage in Cloud Environments

Abstract

The rapid adoption of cloud computing has revolutionized data storage and access, yet it raises significant privacy and security concerns. This study proposes a novel privacy-preserving framework for secure data storage in cloud environments, which integrates advanced cryptographic techniques, access control mechanisms, and secure data outsourcing protocols. Our framework employs homomorphic encryption, attribute-based encryption (ABE), and secure multi-party computation (SMPC) to ensure confidentiality, integrity, and fine-grained access control while minimizing computational overhead. We first analyze current threats in cloud storage, including unauthorized access, data leakage, insider threats, and compromised keys. Then, we design modular components: an encryption module using ABE for attribute-based access policies, a secure query mechanism leveraging homomorphic operations, and a distributed key-management protocol using threshold cryptography. The framework is implemented in a cloud testbed, and performance is evaluated against key metrics—encryption/decryption latency, storage overhead, query efficiency, and scalability. Results demonstrate that our framework incurs only a modest overhead compared to baseline AES-encrypted storage, while providing significantly enhanced privacy guarantees and flexible access controls. Moreover, we validate that the framework resists key compromise and collusion attacks within specified threat models. This work contributes a practical and extensible solution for privacy-preserving cloud storage, suitable for enterprise and healthcare domains handling sensitive data. Future research directions include optimization for large datasets, dynamic attribute revocation, and integration with blockchain-based audit trails.