Privacy-Preserving Data Integrity in Cloud Computing: A Review of Hashing and Verification Techniques
Anil Kumar, Shantala C P - · 2025
Cloud storage has become a crucial component of modern computing, but ensuring data integrity and security remains a pressing concern. Traditional remote data integrity checking (RDIC) mechanisms realize verification using public verification, which incurs privacy concerns. This research explores an identity-based privacy-preserving RDIC scheme using secured hash functions and designated verifiers for enhanced security and efficiency. Leveraging homomorphic verifiable tags and random integer masking, the solution ensures data integrity verification without disclosing sensitive information. Additionally, the integration of merkle hash trees supports dynamic data updates with verifiability and reduced computational complexity. Unlike traditional certificate-based public key infrastructure (PKI) schemes, the approach does not necessitate cumbersome certificate management, making it more efficient and scalable. Security analysis confirms the resilience of the proposed scheme against data leakage, unauthorized access, and integrity attacks. Performance analysis illustrates substantial reductions in computation overhead, improving efficiency for practical cloud storage systems. This research contributes to the development of cloud security frameworks by proposing an identity-based verification mechanism that strikes a balance between security, efficiency, and privacy. The proposed solution ensures data integrity verification is secure while preventing malicious entities from acquiring unauthorized knowledge about stored data, thereby improving trust and reliability in cloud environments.