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Towards dynamic remote data auditing in computational clouds.

Sookhak M, Akhunzada A, Gani A, Khurram Khan M, Anuar NB - ScientificWorldJournal (2014)

Bottom Line: We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete.Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost.The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.

View Article: PubMed Central - PubMed

Affiliation: Center for Mobile Cloud Computing Research (C4MCCR), University of Malaya, 50603 Kuala Lumpur, Malaysia.

ABSTRACT
Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.

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Number or required blocks as a challenge message under different number of data corruptions.
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Related In: Results  -  Collection


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fig6: Number or required blocks as a challenge message under different number of data corruptions.

Mentions: Suppose the CSP modifies m blocks out of the n outsourced blocks. Then, the probability of corrupted blocks is equal to pm = m/n. Let c be the number of blocks that the DO asks to verify the outsourced data in the challenge step and let r be the number of sectors in each block. Let x be a discrete random variable that indicates the number of blocks chosen by the DO that matches the blocks modified by the CSP. We compute the probability that at least one of the blocks picked by the DO matches one of the blocks modified by the server, namely, Px  (x ≥ 1), as follows:(14)Px(x≥1)=1−Px(x=0)=1−(n−mn)·(n−m−1n−1)⋯(n−m−c+1n−c+1)=1−(1−nm)·(1−mn−1)⋯(1−mn−c+1)=1−∏i=0c−1(1−mn−i).On one hand,(15)(1−mn−i)≤(1−mn)⟹∏i=0c−1(1−mn−i)≤(1−mn)c⟹1−∏i=0c−1(1−mn−i)≥1−(1−mn)c.Therefore,(16)⟹(14),(15)Px(x≥1)≥1−(1−mn)c=1−(1−pm)c.Since, each of the blocks consists of r sectors, such probability on the basis of sector corruption ps is computed by(17)pm≥1−(1−ps)r⟹(1−pm)c≤((1−ps)r)c⟹1−(1−pm)c≥1−(1−ps)rc⟹Px(x≥1)≥1−(1−ps)rc.On the other hand,(18)(1−mn−i)≥(1−mn−c+1)⟹∏i=0c−1(1−mn−i)≥(1−mn−c+1)c⟹1−∏i=0c−1(1−mn−i)≤1−(1−mn−c+1)c.Therefore,(19)Px(x≥1)≤1−(1−mn−c+1)c.Then, we can conclude that the probability of misbehavior detection is in(20)1−(1−mn)c≤Px(x≥1)≤1−(1−mn−c+1)c.Suppose the DO divides 1 GB file into 125000 blocks 8 KB and outsources the blocks in the cloud. Figure 6 shows the required number of challenge blocks (c) that are used to detect the different number of corrupted blocks (m) when the probability of misbehaviour detection is collected from a set of Px = {0.7, 0.8, 0.9, 0.99, 0.99999}. For example, if the server modifies 0.1 of the outsourced blocks (n), the DO needs to randomly select 98 block as a challenge to achieve Px of at least 0.99999. As it is clear, by increasing the number of corrupted blocks, the least number of challenge blocks is required to achieve such a probability of detection.


Towards dynamic remote data auditing in computational clouds.

Sookhak M, Akhunzada A, Gani A, Khurram Khan M, Anuar NB - ScientificWorldJournal (2014)

Number or required blocks as a challenge message under different number of data corruptions.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4121005&req=5

fig6: Number or required blocks as a challenge message under different number of data corruptions.
Mentions: Suppose the CSP modifies m blocks out of the n outsourced blocks. Then, the probability of corrupted blocks is equal to pm = m/n. Let c be the number of blocks that the DO asks to verify the outsourced data in the challenge step and let r be the number of sectors in each block. Let x be a discrete random variable that indicates the number of blocks chosen by the DO that matches the blocks modified by the CSP. We compute the probability that at least one of the blocks picked by the DO matches one of the blocks modified by the server, namely, Px  (x ≥ 1), as follows:(14)Px(x≥1)=1−Px(x=0)=1−(n−mn)·(n−m−1n−1)⋯(n−m−c+1n−c+1)=1−(1−nm)·(1−mn−1)⋯(1−mn−c+1)=1−∏i=0c−1(1−mn−i).On one hand,(15)(1−mn−i)≤(1−mn)⟹∏i=0c−1(1−mn−i)≤(1−mn)c⟹1−∏i=0c−1(1−mn−i)≥1−(1−mn)c.Therefore,(16)⟹(14),(15)Px(x≥1)≥1−(1−mn)c=1−(1−pm)c.Since, each of the blocks consists of r sectors, such probability on the basis of sector corruption ps is computed by(17)pm≥1−(1−ps)r⟹(1−pm)c≤((1−ps)r)c⟹1−(1−pm)c≥1−(1−ps)rc⟹Px(x≥1)≥1−(1−ps)rc.On the other hand,(18)(1−mn−i)≥(1−mn−c+1)⟹∏i=0c−1(1−mn−i)≥(1−mn−c+1)c⟹1−∏i=0c−1(1−mn−i)≤1−(1−mn−c+1)c.Therefore,(19)Px(x≥1)≤1−(1−mn−c+1)c.Then, we can conclude that the probability of misbehavior detection is in(20)1−(1−mn)c≤Px(x≥1)≤1−(1−mn−c+1)c.Suppose the DO divides 1 GB file into 125000 blocks 8 KB and outsources the blocks in the cloud. Figure 6 shows the required number of challenge blocks (c) that are used to detect the different number of corrupted blocks (m) when the probability of misbehaviour detection is collected from a set of Px = {0.7, 0.8, 0.9, 0.99, 0.99999}. For example, if the server modifies 0.1 of the outsourced blocks (n), the DO needs to randomly select 98 block as a challenge to achieve Px of at least 0.99999. As it is clear, by increasing the number of corrupted blocks, the least number of challenge blocks is required to achieve such a probability of detection.

Bottom Line: We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete.Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost.The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.

View Article: PubMed Central - PubMed

Affiliation: Center for Mobile Cloud Computing Research (C4MCCR), University of Malaya, 50603 Kuala Lumpur, Malaysia.

ABSTRACT
Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.

Show MeSH