A position-aware Merkle tree for dynamic cloud data integrity verification

2015 ◽  
Vol 21 (8) ◽  
pp. 2151-2164 ◽  
Author(s):  
Jian Mao ◽  
Yan Zhang ◽  
Pei Li ◽  
Teng Li ◽  
Qianhong Wu ◽  
...  
Keyword(s):  
Data Integrity ◽  
Merkle Tree ◽  
Cloud Data ◽  
Integrity Verification

scholarly journals Cloud Data Integrity Verification Algorithm for Sustainable Accounting Informatization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Yang
Keyword(s):  
Big Data ◽  
Execution Time ◽  
Cloud Storage ◽  
Computing Time ◽  
Data Integrity ◽  
Cloud Data ◽  
Integrity Verification ◽  
Verification Algorithm ◽  
Advantages And Disadvantages ◽  
Better Than

In recent years, people have paid more and more attention to cloud data. However, because users do not have absolute control over the data stored on the cloud server, it is necessary for the cloud storage server to provide evidence that the data are completely saved to maintain their control over the data. Give users all management rights, users can independently install operating systems and applications and can choose self-service platforms and various remote management tools to manage and control the host according to personal habits. This paper mainly introduces the cloud data integrity verification algorithm of sustainable computing accounting informatization and studies the advantages and disadvantages of the existing data integrity proof mechanism and the new requirements under the cloud storage environment. In this paper, an LBT-based big data integrity proof mechanism is proposed, which introduces a multibranch path tree as the data structure used in the data integrity proof mechanism and proposes a multibranch path structure with rank and data integrity detection algorithm. In this paper, the proposed data integrity verification algorithm and two other integrity verification algorithms are used for simulation experiments. The experimental results show that the proposed scheme is about 10% better than scheme 1 and about 5% better than scheme 2 in computing time of 500 data blocks; in the change of operation data block time, the execution time of scheme 1 and scheme 2 increases with the increase of data blocks. The execution time of the proposed scheme remains unchanged, and the computational cost of the proposed scheme is also better than that of scheme 1 and scheme 2. The scheme in this paper not only can verify the integrity of cloud storage data but also has certain verification advantages, which has a certain significance in the application of big data integrity verification.

Merkle Tree and Blockchain-Based Cloud Data Auditing

2020 ◽  
Vol 10 (3) ◽  
pp. 54-66
Author(s):  
Arun Prasad Mohan ◽  
Mohamed Asfak R. ◽  
Angelin Gladston
Keyword(s):  
Data Integrity ◽  
Service Provisioning ◽  
Merkle Tree ◽  
Cloud Data ◽  
Integrity Checking ◽  
Dataset Size ◽  
Data Auditing ◽  
Core Idea ◽  
Cloud Audit ◽  
Verification Techniques

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.

scholarly journals Blockchain-Based Cloud Data Integrity Verification Scheme with High Efficiency

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Gaopeng Xie ◽  
Yuling Liu ◽  
Guojiang Xin ◽  
Qiuwei Yang
Keyword(s):  
Cloud Storage ◽  
Large Scale ◽  
High Efficiency ◽  
Security Analysis ◽  
Data Integrity ◽  
Verification Scheme ◽  
Cloud Data ◽  
Integrity Verification ◽  
Computational Overhead ◽  
Cuckoo Filter

With the large-scale application of cloud storage, how to ensure cloud data integrity has become an important issue. Although many methods have been proposed, they still have their limitations. This paper improves some defects of the previous methods and proposes an efficient cloud data integrity verification scheme based on blockchain. In this paper, we proposed a lattice signature algorithm to resist quantum computing and introduced cuckoo filter to simplify the computational overhead of the user verification phase. Finally, the decentralized blockchain network is introduced to replace traditional centralized audit to publicize and authenticate the verification results, which improves the transparency and the security of this scheme. Security analysis shows that our scheme can resist malicious attacks and experimental results show that our scheme has high efficiency, especially in the user verification phase.

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