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

PROJECT TITLE:

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

ABSTRACT:

Using Cloud Computing, people will store their knowledge on remote servers and permit data access to public users through the cloud servers. As the outsourced information are likely to contain sensitive privacy information, they're typically encrypted before uploaded to the cloud. This, but, considerably limits the usability of outsourced information thanks to the problem of looking out over the encrypted data. During this paper, we tend to address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we tend to introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and customized user experience. Second, we tend to develop a practical and very economical multi-keyword search scheme. The proposed theme will support difficult logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we have a tendency to further use the classified sub-dictionaries technique to achieve higher potency on index building, trapdoor generating and query. Lastly, we have a tendency to analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the $64000-world dataset, we have a tendency to 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 better performance in terms of functionality, question complexity and potency.

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