Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system
Most of the existing outsourced encrypted data schemes are retrieved based on the query keyword entered by authorised users. However, with the increase of the data scale in the cloud storage system, the retrieval efficiency of existing solutions has not been significantly improved. In this paper, a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2020.1753175 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736623341699072 |
|---|---|
| author | Tingting Xiao Dezhi Han Junhui He Kuan-Ching Li Rodrigo Fernandes de Mello |
| author_facet | Tingting Xiao Dezhi Han Junhui He Kuan-Ching Li Rodrigo Fernandes de Mello |
| author_sort | Tingting Xiao |
| collection | DOAJ |
| description | Most of the existing outsourced encrypted data schemes are retrieved based on the query keyword entered by authorised users. However, with the increase of the data scale in the cloud storage system, the retrieval efficiency of existing solutions has not been significantly improved. In this paper, a multi-keyword ranked search scheme for ciphertext based on mapping set matching (MSMR) is proposed, where (1) The private cloud server matches the keyword numbering set corresponding to the document index vector and the keyword numbering set corresponding to the query vector and sends the document identifier of the matching keyword numbering to the public cloud server. The public cloud server filters the documents irrelevant to the query request according to the document identifier corresponding to the matching keyword numbering, which effectively reduces the time spent in calculating the correlation score, and (2) the document index vector and query vector are segmented before encrypting them out, reducing the time to construct such vectors. Theoretical analysis shows that the proposed scheme is secure in the known ciphertext model. Experimental results confirm that whenever the data scale grows, the improvement of MSMR retrieval efficiency is more significant. |
| format | Article |
| id | doaj-art-8407cd45dec140f9aec9d6fd34a7058b |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-8407cd45dec140f9aec9d6fd34a7058b2025-08-20T03:07:13ZengTaylor & Francis GroupConnection Science0954-00911360-04942021-01-013319511210.1080/09540091.2020.17531751753175Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage systemTingting Xiao0Dezhi Han1Junhui He2Kuan-Ching Li3Rodrigo Fernandes de Mello4Department of Information Engineering, Shanghai Maritime UniversityDepartment of Information Engineering, Shanghai Maritime UniversityDepartment of Information Engineering, Shanghai Maritime UniversityDepartment of Computer Science and Information Engineering, Providence UniversityDepartment of Computer Science, University of Sao PauloMost of the existing outsourced encrypted data schemes are retrieved based on the query keyword entered by authorised users. However, with the increase of the data scale in the cloud storage system, the retrieval efficiency of existing solutions has not been significantly improved. In this paper, a multi-keyword ranked search scheme for ciphertext based on mapping set matching (MSMR) is proposed, where (1) The private cloud server matches the keyword numbering set corresponding to the document index vector and the keyword numbering set corresponding to the query vector and sends the document identifier of the matching keyword numbering to the public cloud server. The public cloud server filters the documents irrelevant to the query request according to the document identifier corresponding to the matching keyword numbering, which effectively reduces the time spent in calculating the correlation score, and (2) the document index vector and query vector are segmented before encrypting them out, reducing the time to construct such vectors. Theoretical analysis shows that the proposed scheme is secure in the known ciphertext model. Experimental results confirm that whenever the data scale grows, the improvement of MSMR retrieval efficiency is more significant.http://dx.doi.org/10.1080/09540091.2020.1753175cloud storage systemciphertext retrievaloutsourced encrypted datasemantic extensionmapping set matching |
| spellingShingle | Tingting Xiao Dezhi Han Junhui He Kuan-Ching Li Rodrigo Fernandes de Mello Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system Connection Science cloud storage system ciphertext retrieval outsourced encrypted data semantic extension mapping set matching |
| title | Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system |
| title_full | Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system |
| title_fullStr | Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system |
| title_full_unstemmed | Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system |
| title_short | Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system |
| title_sort | multi keyword ranked search based on mapping set matching in cloud ciphertext storage system |
| topic | cloud storage system ciphertext retrieval outsourced encrypted data semantic extension mapping set matching |
| url | http://dx.doi.org/10.1080/09540091.2020.1753175 |
| work_keys_str_mv | AT tingtingxiao multikeywordrankedsearchbasedonmappingsetmatchingincloudciphertextstoragesystem AT dezhihan multikeywordrankedsearchbasedonmappingsetmatchingincloudciphertextstoragesystem AT junhuihe multikeywordrankedsearchbasedonmappingsetmatchingincloudciphertextstoragesystem AT kuanchingli multikeywordrankedsearchbasedonmappingsetmatchingincloudciphertextstoragesystem AT rodrigofernandesdemello multikeywordrankedsearchbasedonmappingsetmatchingincloudciphertextstoragesystem |