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Efficient privacy‐preserving scheme supporting disjunctive multi‐keyword search with ranking
Author(s) -
Handa Rohit,
Rama Krishna C.,
Aggarwal Naveen
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5450
Subject(s) - computer science , inverted index , encryption , scheme (mathematics) , outsourcing , cloud computing , inefficiency , index (typography) , data mining , ranking (information retrieval) , information retrieval , search engine indexing , computer security , mathematics , mathematical analysis , world wide web , political science , law , economics , microeconomics , operating system
Summary Information storage and retrieval from the cloud is growing continuously unabated. The cost‐efficient solutions offered by the cloud providers to the end‐users have motivated them to outsource their confidential data to the cloud. Outsourcing confidential data leads to enhanced privacy risks due to disclosure of sensitive information to adversaries. To handle this disclosure of information, encryption is preferred, but it hinders the efficient searching on the documents. The existing searchable encryption schemes either focused on optimization of search time or improvement of search efficiency. To solve this trade‐off between search time and search efficiency, we propose an efficient disjunctive search scheme using non‐positional inverted index. To the best of our knowledge, there is no searchable encryption scheme based on the non‐positional inverted index in the literature. Thus, we first propose a basic scheme based on a non‐positional inverted index to search the desired keywords and highlight its inefficiency in terms of high search time required. To perform efficient searching, an extended search scheme is proposed using keyword binning, which reduces comparisons required and improves the search time. The extended search scheme has recall of 100% and precision 99.75%. The experimental analysis of the proposed scheme on real datasets proves that the proposed scheme is privacy‐preserving and efficient.