Enabling Fine-grained Multi-keyword Search Supporting Classified Sub-dictionaries over Encrypted Cloud Data - 2015


Using cloud computing, individuals can store their data on remote servers and allow knowledge access to public users through the cloud servers. As the outsourced data are probably to contain sensitive privacy information, they're typically encrypted before uploaded to the cloud. This, but, considerably limits the usability of outsourced data because of the issue of searching over the encrypted information. In this paper, we tend to address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud information. Our original contributions are three-fold. 1st, we introduce the relevance scores and preference factors upon keywords that enable the precise keyword search and personalised user expertise. Second, we tend to develop a sensible and very economical multi-keyword search theme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we have a tendency to more employ the classified sub-dictionaries technique to attain better efficiency on index building, trapdoor generating and query. Lastly, we tend to analyze the safety of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through in depth experiments using the important-world dataset, we validate the performance of the proposed schemes. Each the protection analysis and experimental results demonstrate that the proposed schemes will achieve the identical security level comparing to the prevailing ones and higher performance in terms of functionality, query complexity and efficiency.

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