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Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage

Li, Hongwei, Liu, Dongxiao, Dai, Yuanshun, Luan, Tom H. and Shen, Xuemin Sherman 2015, Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage, IEEE transactions on emerging topics in computing, vol. 3, no. 1, pp. 127-139, doi: 10.1109/TETC.2014.2371239.

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Title Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage
Author(s) Li, Hongwei
Liu, Dongxiao
Dai, Yuanshun
Luan, Tom H.
Shen, Xuemin Sherman
Journal name IEEE transactions on emerging topics in computing
Volume number 3
Issue number 1
Start page 127
End page 139
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-03
ISSN 2168-6750
Keyword(s) cloud computing
searchable encryption
multi-keyword ranked search
blind storage
access pattern
Summary In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.
Language eng
DOI 10.1109/TETC.2014.2371239
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083992

Document type: Journal Article
Collection: School of Information Technology
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Scopus Citation Count Cited 26 times in Scopus
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