TY - GEN
T1 - Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking
AU - Sun, Wenhai
AU - Wang, Bing
AU - Cao, Ning
AU - Li, Ming
AU - Lou, Wenjing
AU - Hou, Y. Thomas
AU - Li, Hui
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - cloud computing
KW - multi-keyword search
KW - privacy-preserving search
KW - similarity-based ranking
UR - http://www.scopus.com/inward/record.url?scp=84877939257&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877939257&partnerID=8YFLogxK
U2 - 10.1145/2484313.2484322
DO - 10.1145/2484313.2484322
M3 - Conference contribution
AN - SCOPUS:84877939257
SN - 9781450317672
T3 - ASIA CCS 2013 - Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security
SP - 71
EP - 81
BT - ASIA CCS 2013 - Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security
T2 - 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, ASIA CCS 2013
Y2 - 8 May 2013 through 10 May 2013
ER -