PROJECT TITLE :

DNA Similarity Search with Access Control over Encrypted Cloud Data

ABSTRACT:

DNA similarity search has seen widespread use in human genomic research, including applications in DNA marking, genomic sequencing, and the prediction of genetic diseases. In the meantime, users are increasingly leaning toward storing DNA data on the cloud in order to save money on local costs as a result of the explosive growth of data. However, due to the high level of confidentiality associated with DNA data, the government is required to exercise stringent control over the acquisition and utilization of this information. Encrypting DNA data prior to uploading it to the cloud is one potential solution that could be considered. Despite the fact that private DNA similarity query is a topic of active research, the current state of the art does not produce satisfactory results in terms of safety, functionality, or efficiency. This article presents EFSS, which is an efficient and fine-grained similarity search scheme over encrypted DNA data. It was developed by us. To be more specific, we begin by developing an approximation algorithm for the purpose of accurately calculating the edit distances that exist between two sequences. Second, we developed an innovative Boolean search strategy to accomplish complex logic queries on genes, such as mixed "AND" and "NO" operations. Third, our EFSS includes support for data access control thanks to a polynomial-based design that has been modified in one of its variants. In addition to this, the K-means clustering algorithm is utilized in order to further enhance the effectiveness of the execution. In conclusion, a comprehensive security analysis and a large number of experiments show that EFSS has a superior performance to that of other existing schemes.