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Secure multi-server-aided data deduplication in cloud computing

delete2015-12-01
delete62
PRE
AI
M
Meixia Miao
J
Jianfeng Wang
李晖 cover
李晖 (Hui Li)
陈晓峰 cover
陈晓峰 (Xiaofeng Chen) *
DOI:10.1016/j.pmcj.2015.03.002delete
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Abstract

Abstract

En 中文
Cloud computing enables on-demand and ubiquitous access to a centralized pool of configurable resources such as networks, applications, and services. This makes that huge of enterprises and individual users outsource their data into the cloud server. As a result, the data volume in the cloud server is growing extremely fast. How to efficiently manage the ever-increasing datum is a new security challenge in cloud computing. Recently, secure deduplication techniques have attracted considerable interests in the both academic and industrial communities. It can not only provide the optimal usage of the storage and network bandwidth resources of cloud storage providers, but also reduce the storage cost of users. Although convergent encryption has been extensively adopted for secure deduplication, it inevitably suffers from the off-line brute-force dictionary attacks since the message usually can be predictable in practice. In order to address the above weakness, the notion of DupLESS was proposed in which the user can generate the convergent key with the help of a key server. We argue that the DupLESS does not work when the key server is corrupted by the cloud server. In this paper, we propose a new multi-server-aided deduplication scheme based on the threshold blind signature, which can effectively resist the collusion attack between the cloud server and multiple key servers. Furthermore, we prove that our construction can achieve the desired security properties. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Data deduplication
Server-aided
Brute-force attack
Verifiable secret sharing
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Journal

Pervasive and Mobile Computing cover
Pervasive and Mobile Computing
IF:
3.5
Papers:
1.5K
Citations:
2.2K

Organization

X
Xidian University
Scholars:
2.4W
Papers: 1.9W
Citations: 9.7K
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