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Security and Communication Networks
Volume 2017, Article ID 1923476, 17 pages
Research Article

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

1College of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 200013, China
2Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210013, China
3School of Computer Science and IT, RMIT University, Melbourne, VIC 3001, Australia

Correspondence should be addressed to Dai Hua; nc.ude.tpujn@auhiad

Received 9 February 2017; Accepted 22 May 2017; Published 11 July 2017

Academic Editor: Xiangyang Luo

Copyright © 2017 Zhu Xiangyang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.