Conjunctive and Fuzzy Queries over Encrypted Data with Efficient and Verifiable Results in the Cloud

PROJECT TITLE :

Achieve Efficient and Verifiable Conjunctive and Fuzzy Queries over Encrypted Data in Cloud

ABSTRACT:

Searchable encryption, also known as SE, has recently garnered a lot of attention and has been widely suggested for use in encrypted cloud storage in response to the growing demand for data to be searchable even after it has been encrypted. In the majority of SE-based cloud storage systems, it is customarily assumed that the cloud server is trustworthy but inquisitive. This means that the cloud server should adhere to the protocol in order to provide users with valid and comprehensive search results. On the other hand, this trust assumption is not always accurate because of unforeseen circumstances such as misconfigurations and malfunctions in the system. For this reason, the function of verifying the search results becomes absolutely necessary to ensure the success of cloud storage systems that are based on SE. Because of this, numerous verifiable SE schemes have been proposed; however, they either do not support query operators "OR," "AND," "," and "?" simultaneously or demand a great deal of time-consuming work. In this paper, we propose a new verifiable SE scheme for encrypted cloud storage with the goal of addressing the issue that has been brought to light. The proposed method is characterized by integrating various techniques, such as keyed-hash message authentication code and symmetric key encryption, for the purpose of achieving efficient and verifiable conjunctive and fuzzy queries over encrypted data stored in the cloud. These techniques include bitmap index, radix tree, format preserving encryption, keyed-hash message authentication code, and symmetric key encryption. The results of a comprehensive security analysis demonstrate that our proposed system maintains both the verifiability of search results and the confidentiality of the data being searched. In addition, we carry out a large number of experiments, and the findings show that our proposed scheme is effective and appropriate for users to retrieve their data from the cloud onto their mobile devices.

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