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Security and Communication Networks
Volume 2017, Article ID 1923476, 17 pages
https://doi.org/10.1155/2017/1923476
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.

How to Cite this Article

Zhu Xiangyang, Dai Hua, Yi Xun, Yang Geng, and Li Xiao, “MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data,” Security and Communication Networks, vol. 2017, Article ID 1923476, 17 pages, 2017. https://doi.org/10.1155/2017/1923476.