Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system

• Published in: Connection science Vol. 33; no. 1; pp. 95 - 112
• Main Authors: Xiao, Tingting, Han, Dezhi, He, Junhui, Li, Kuan-Ching, de Mello, Rodrigo Fernandes
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.01.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; ciphertext retrieval; Cloud computing; Cloud storage system; Correlation analysis; Data storage; Encryption; Keywords; Mapping; mapping set matching; Matching; outsourced encrypted data; Queries; Retrieval; semantic extension
• ISSN: 0954-0091; 1360-0494
• DOI: 10.1080/09540091.2020.1753175

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.