Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries Over Encrypted Cloud Data

Abstract

Abstract

Huge numbers of papers are transferred to the cloud for easier access and lower administration costs as cloud computing gains popularity. Individuals can utilize cloud computing to store their data on distant servers and grant public users access to the data via the cloud servers. Outsourced data are normally encrypted before being posted to the clod since they are likely to include sensitive privacy information. The difficulty of sifting through the encrypted data, however, severely restricts the utility of the outsourced data. In this research, we introduce the fine-grained multi-keyword search algorithms over encrypted clod data to overcome this problem. We made three original contributions. In order to provide exact keyword search and a tailored user experience, we initially present the relevance source plus preference factor upon keywords. Furthermore, we create a useful and very effective multi-keyword search strategy. The suggested system can enable complex logic searches using mixed "AND," "OR," and "NO" keyword operations. Thirdly, in order to improve the efficiency of index building and query generation, we also use the classified sub-dictionaries technique. Finally, we examine the security of the suggested strategies with regard to of document confidentiality, index alongside trapdoor privacy protection, as well as trapdoor unlinkability. We confirm that the suggested method performs better when it comes to of functionality, query complexity, yet efficiency than the ones currently in use plus can reach the same security level through rigorous tests on a real-world dataset.  We confirm that the suggested method performs better with regard to functionality, query complexity, and efficiency than the existing ones and can reach the same security level through rigorous tests on a real-world dataset.