ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA
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This paper presents fine-grained multi-keyword search schemes for encrypted cloud data, addressing the challenges of searching such data while protecting sensitive information. The contributions include the introduction of relevance scores for personalized experiences, an efficient multi-keyword search scheme with complex logic operations, and the use of classified sub-dictionaries for improved indexing and querying. Security analysis and experiments confirm the proposed schemes offer comparable security to existing methods with enhanced performance in functionality and efficiency.
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ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA
- 1. ENABLING FINE-GRAINED MULTI-KEYWORD SEARCH SUPPORTING CLASSIFIED SUB-DICTIONARIES OVER ENCRYPTED CLOUD DATA ABSTRACT Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi- keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we 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 real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.