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Lightweight privacy-preserving cloud data provenance with federated blockchain
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DOI：10.1016/j.bcra.2026.100536.png)
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Abstract
En 中文
With the exponential growth in the demand for cloud computing, data security and integrity are becoming critical issues when the data is outsourced to the cloud. To address these issues, this paper proposes a lightweight privacy-preserving federated blockchain framework for cloud data. Unlike traditional mechanisms, the proposed framework minimizes computational and storage overhead by storing only encrypted metadata on the blockchain, which ensures end-to-end data encryption and pseudonymity of user identities. Experimental results demonstrate the validity of the framework’s scalability and efficiency, achieve a 47% reduction in key generation time, 40% faster data registration, and a 50% decrease in auditing overhead compared to state-of-the-art approaches. The framework is evaluated under simulated multi-cloud environments. Therefore, the proposed work provides a novel, scalable, and privacy-centric solution for secure cloud data management in the Internet of Things era, setting a new benchmark for lightweight and privacy-enhanced data provenance in distributed environments.
Keywords:
Merkle Tree
Blockchain
Data provenance
Zero-knowledge proof
Journal
IF:
5.6
Papers:
310
Citations:
754
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Cited Papers