Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data

Li, Hongwei, Yang, Yi, Luan, Tom H., Liang, Xiaohui, Zhou, Liang and Shen, Xuemin (Sherman) 2016, Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data, IEEE transactions on dependable and secure computing, vol. 13, no. 3, pp. 312-325, doi: 10.1109/TDSC.2015.2406704.

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Title Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data
Author(s) Li, Hongwei
Yang, Yi
Luan, Tom H.
Liang, Xiaohui
Zhou, Liang
Shen, Xuemin (Sherman)
Journal name IEEE transactions on dependable and secure computing
Volume number 13
Issue number 3
Start page 312
End page 325
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-05-01
ISSN 1545-5971
Keyword(s) searchable encryption
cloud computing
Summary Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.
Language eng
DOI 10.1109/TDSC.2015.2406704
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 ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083979

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