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Achieving Efficient Secure Deduplication With User-Defined Access Control in Cloud

delete2022-01-01
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PRE
AI
X
Xue Yang
Rongxing Lu cover
Rongxing Lu (Rongxing Lu) *
邵
邵俊 (Jun Shao)
X
Xiaohu Tang
A
Ali A. Ghorbani
DOI:10.1109/TDSC.2020.2987793delete
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Abstract

Abstract

En 中文
Cloud storage as one of the most important services of cloud computing which significantly facilitates cloud users to outsource their data to the cloud for storage and share them with authorized users. In cloud storage, secure deduplication has been widely investigated as it can eliminate the redundancy over the encrypted data to reduce storage space and communication overhead. Regarding the security and privacy, many existing secure deduplication schemes generally focus on achieving the following properties: data confidentiality, tag consistency, access control, and resistance to brute-force attacks. However, as far as we know, none of them can achieve these four requirements at the same time. To overcome this shortcoming, in this article, we propose an efficient secure deduplication scheme that supports user-defined access control. Specifically, by allowing only the cloud service provider to authorize data access on behalf of data owners, our scheme can maximally eliminate duplicates without violating the security and privacy of cloud users. Detailed security analysis shows that our authorized secure deduplication scheme achieves data confidentiality and tag consistency while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme outperforms the existing competing schemes, in terms of computational, communication and storage overheads as well as the effectiveness of deduplication.
Keywords:
Cloud computing
Cryptography
Access control
Computational modeling
Resistance
Data privacy
Secure deduplication
access control
authorized deduplication system
tag consistency
brute-force attacks
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Journal

IEEE Transactions on Dependable and Secure Computing cover
IEEE Transactions on Dependable and Secure Computing
IF:
7.5
Papers:
2.5K
Citations:
9.6K

Organization

T
tsinghua university
Scholars:
11.9W
Papers: 10.0W
Citations: 137
U
University of New Brunswick
Scholars:
4.0K
Papers: 4.2K
Citations: 6.3K
P
Peng Cheng Laboratory
Scholars:
1.7K
Papers: 1.8K
Citations: 2.0K
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