please send the dfd for fuzzy keyword search over encrypted data in cloud computing.

The searchable encryption is used to support searches of encrypted data stored on servers in the cloud. Traditional search encryption only supports the search for exact keywords instead of a more flexible fuzzy keyword search. To solve this problem, a recent emerging paradigm, called diffuse keyword search encryption, has been proposed. There have been some proposals designed for fuzzy keyword search in the symmetric key configuration, but none of the efficient schemas in the public key configuration. In this article, we propose a new interactive public key encryption primitive with fuzzy keyword search (IPEFKS), which supports the efficient search of fuzzy keywords over encrypted data in the public key configuration. We build and implement a scheme based on IPEFKS homomorphic encryption. To compare this scheme with existing ones, we implemented LWW-FKS, which, to the best of our knowledge, is the most efficient among existing schemes. The experimental results show that IPEFKS is much more efficient than LWW-FKS.

With the increasing rate of growth and the adaptation of cloud computing, increasingly sensitive information is being centralised in the cloud. For the protection of valuable property information, the data must be encrypted prior to subcontracting. Existing search techniques allow the user to search for encrypted data using keywords, but these techniques only explain the exact keyword search. There is no tolerance for typographical errors and format inconsistencies that are the normal behaviour of the user. This makes data storage and the use of a very difficult task effective, making the user search very frustrating and inefficient. In this article, we focus on secure storage using Advanced Encryption Standard (AES) and information retrieval by performing diffuse keyword search on these encrypted data. We propose the implementation of an advanced fuzzy keyword search mechanism called the wildcard-based technique that returns matched files when users' search entries exactly match the predefined keywords or closest matching files based on the semantics of Keywords of similarity. In the proposed solution, we exploit editing distance to quantify keyword similarity and developed an efficient technique for building fuzzy keyword sets that focus on reducing overall storage and rendering costs.