Abstract:
DNA marking, genomic sequencing, and genetic disease prediction use DNA similarity search. Users are also storing DNA data on the cloud to save money as data grows rapidly. DNA data is sensitive, so the government strictly controls its acquisition and use.
Encrypting DNA data before cloud storage is one option. Private DNA similarity query has been a research topic, but current results lack security, functionality, and efficiency. EFSS is a fast, fine-grained similarity search scheme for encrypted DNA data.
First, we create an approximation algorithm to efficiently calculate edit distances between sequences. Second, we proposed a new Boolean search strategy for complex logic queries like gene “AND” and “NO” operations. Third, a polynomial-based design in our EFSS supports data access control.
The K-means clustering algorithm improves execution efficiency. Security analysis and extensive experiments show that EFSS outperforms other schemes.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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