MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data

Xiangyang, Z, Hua, D, Yi, X, Geng, Y and Xiao, L 2017, 'MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data', Security and Communication Networks, vol. 9, no. 4, pp. 1-17.


Document type: Journal Article
Collection: Journal Articles

Title MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data
Author(s) Xiangyang, Z
Hua, D
Yi, X
Geng, Y
Xiao, L
Year 2017
Journal name Security and Communication Networks
Volume number 9
Issue number 4
Start page 1
End page 17
Total pages 17
Publisher John Wiley and Sons
Abstract With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.
Subject Computation Theory and Mathematics not elsewhere classified
DOI - identifier 10.1155/2017/1923476
Copyright notice Copyright © 2017 Zhu Xiangyang et al. Creative Commons Attribution License
ISSN 1939-0114
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