NDLI: Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking

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Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking

Content Provider	ACM Digital Library
Author	Lou, Wenjing Sun, Wenhai Li, Ming Hou, Y. Thomas Cao, Ning Li, Hui Wang, Bing
Abstract	With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.
Starting Page	71
Ending Page	82
Page Count	12
File Format	PDF
ISBN	9781450317672
DOI	10.1145/2484313.2484322
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-05-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cloud computing Privacy-preserving search Multi-keyword search Similarity-based ranking
Content Type	Text
Resource Type	Article

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