A Privacy-Preserving Framework for Scalable Data Integrity Verification in Cloud Storage Using Zero-Knowledge Proofs

Abstract

Cloud storage systems have become central to data-driven industries due to their flexibility and scalability. However, ensuring the integrity and confidentiality of outsourced data remains a major concern, particularly in multi-tenant and dynamic cloud environments. This paper proposes a novel privacy-preserving framework that integrates Zero-Knowledge Proofs (ZKP), Pedersen Commitments, and bulk segmentation for efficient and scalable data integrity verification. Unlike traditional approaches, our framework enables Third-Party Auditors (TPAs) to verify cloud-stored data without exposing sensitive information. It is designed to support dynamic operations, detect insider and external threats proactively, and minimize computational overhead through segment-level auditing. Implementation and evaluation using Amazon S3 and DynamoDB demonstrate the framework’s practical viability, low communication cost, and robust tamper detection capabilities.