An Efficient Methodology to Audit Data in Cloud Storage to Ensure Reliability and Integrity Using Secured Crypto Schemes

Cloud storage has revolutionized how organizations and individuals store and manage data by providing scalable and cost-effective solutions. However, the primary concern remains ensuring data integrity, reliability, and security. This paper explores an efficient methodology to audit cloud storage data, using a combination of secured cryptographic schemes. Techniques such as Third-Party Auditing (TPA), Dynamic Provable Data Possession (DPDP), Random Sampling, and Sentinel-based Proof of Retrievability (POR) are employed to guarantee data accuracy and integrity. The methods are evaluated based on audit time, integrity verification rates, and storage overhead. TPA achieved a near-perfect accuracy of 99-100% in data integrity verification but incurred higher audit times. Random Sampling, with a 92-96% accuracy rate, offered significantly reduced audit time while maintaining high integrity verification. Sentinel-based POR exhibited 98-100% accuracy and provided an excellent balance between security and audit performance. Overall, Random Sampling and POR were the most efficient techniques, making them suitable for large datasets. This methodology ensures robust cloud storage auditing, significantly improving performance, reducing overhead, and preserving user privacy.