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Merkle Tree and Blockchain-Based Cloud Data Auditing

Merkle Tree and Blockchain-Based Cloud Data Auditing

Arun Prasad Mohan, Mohamed Asfak R., Angelin Gladston
Copyright: © 2020 |Volume: 10 |Issue: 3 |Pages: 13
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781799807759|DOI: 10.4018/IJCAC.2020070103
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MLA

Mohan, Arun Prasad, et al. "Merkle Tree and Blockchain-Based Cloud Data Auditing." IJCAC vol.10, no.3 2020: pp.54-66. http://doi.org/10.4018/IJCAC.2020070103

APA

Mohan, A. P., Mohamed Asfak R., & Gladston, A. (2020). Merkle Tree and Blockchain-Based Cloud Data Auditing. International Journal of Cloud Applications and Computing (IJCAC), 10(3), 54-66. http://doi.org/10.4018/IJCAC.2020070103

Chicago

Mohan, Arun Prasad, Mohamed Asfak R., and Angelin Gladston. "Merkle Tree and Blockchain-Based Cloud Data Auditing," International Journal of Cloud Applications and Computing (IJCAC) 10, no.3: 54-66. http://doi.org/10.4018/IJCAC.2020070103

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Abstract

Cloud computing is the fastest growing and most promising field in the service provisioning segment. It has become a challenging task to provide security in the cloud. The purpose of this article is to suggest a better and efficient integrity verification technique for data referred to as cloud audit. The deployment of cloud storage services has significant benefits in the management of data for users. However, this raises many security concerns, and one of them is data integrity. Though public verification techniques serve the purpose they are vulnerable to procrastinating auditors who may not perform verifications on time. In this article, a cloud data auditing system is proposed. The proposed cloud data auditing system integrates Merkle Tree-based Cloud audit and the blockchain-based audit recording system, thus the core idea is to record each verification result into a blockchain as a transaction. Utilizing the time-sensitive nature of blockchain, the verifications are time-stamped after the corresponding transaction is recorded into the blockchain, which enables users to check whether auditors have performed the verifications at the prescribed time. The proposed cloud data auditing system is experimentally validated. The investigations with varied dataset size revealed less time taken, on an average of 0.25 milliseconds with the use of Merkle Tree. Further results reveal consistency of the data integrity checking.

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