MPA: Lightweight and Updatable Integrity Auditing for Decentralized Storage Using Merkle Trees and Polynomial Commitments

Yongliang Xu, Hang Cheng, Jingyu Zheng, Xinpeng Zhang, Huaxiong Wang

Abstract: With the growing demand for outsourcing data to decentralized storage systems, ensuring the integrity of outsourced data becomes a critical challenge. Existing auditing schemes, however, often assume single-copy or centralized models, and suffer from inefficiency, lack of public verifiability, or poor scalability in multi-replica settings. To address these limitations, we propose MPA, a lightweight and publicly verifiable auditing scheme tailored for multi-copy cloud storage. By integrating polynomial commitment schemes with Merkle trees, our design achieves efficient block-level integrity verification while enabling dynamic updates. To mitigate collusion between cloud service providers, each data copy is uniquely encrypted, and the audit process supports simultaneous verification across multiple providers. Furthermore, we introduce an optimized batch auditing mechanism that allows the verifier to aggregate proofs across different files and providers, reducing both computation and communication overhead. To enhance audit transparency and unpredictability, we adopt a blockchain-assisted challenge generation protocol based on commit-and-reveal randomness. Theoretical analysis and performance evaluation demonstrate that MPA achieves strong security guarantees under standard assumptions, while significantly outperforming existing solutions in terms of efficiency and scalability.

External IDs:dblp:journals/tifs/XuCZZW26