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Secure multi-server-aided data deduplication in cloud computing
Miao M., Wang J., Li H., Chen X. Pervasive and Mobile Computing24 (C):129-137,2015.Type:Article
Date Reviewed: Jul 28 2016

This detailed paper on secure multiserver-aided data deduplication computing gives a quick overview of cloud computing and then delves into the main topic of secure deduplication. The value the paper sets to fix is the vulnerability of convergent encryption as an option for secure deduplication because it’s vulnerable to offline brute-force dictionary attacks. The author’s solution sets to design a deduplication scheme that can withstand a brute-force attack.

In the design, some of the preliminaries discussed include bilinear pairings, gap Diffie-Hellman groups, verifiable secret sharing, threshold blind signature, and convergent encryption. The solution considers both an external attacker and an internal attacker to the deduplication systems with two technicalities on security convergent keys and data confidentiality.

A major concern of the proposed system is the security assumptions made. The authors assume that “the underlying building blocks are secure.” This includes a convergent encryption scheme, the threshold blind signatures, and proof of work (PoW) schemes. This contradicts their conclusion that the schemes achieve the desired security goals.

Finally, the authors discuss the steps in designing the multiserver-aided deduplication scheme that enables the user to “generate the convergent key with the help of a key server.” Another major deficiency is the lack of a practical application to prove that the provided solution was implemented and actually worked. The audience of this paper needs to have some advanced understanding of the different algorithms used in the main equations to be able to prove their applicability because the paper is technical in nature. This work will be most appreciated by researchers within the same field in their quest to find another solution for the same problem.

Reviewer:  Fredrick Dande Review #: CR144646 (1611-0807)
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