DNA Similarity Search With Access Control Over Encrypted Cloud Data

Abstract

DNA similarity search has been widely applied in human genomic studies including DNA marking, genomic sequencing and genetic disease prediction. Meanwhile, with the explosive growth of data, users are increasingly inclining to store DNA data on the cloud for saving local cost. However, the high sensitivity of DNA data has forced the government to strictly control its acquisition and utilization. One potential solution is to encrypt DNA data before outsourcing them to the cloud. Nevertheless, private DNA similarity query has been an active research issue, state-of-the-art results are still defective in security, functionality and efficiency. In this paper, we propose EFSS, an Efficient and Fine-grained Similarity Search scheme over encrypted DNA data. In specific, firstly, we design an approximation algorithm to efficiently calculate the edit distances between two sequences. Secondly, we put forward a novel Boolean search strategy to achieve complicated logic queries such as mixed AND and NO operations on genes. Thirdly, data access control is also supported in our EFSS through a variant of polynomial based design. Moreover, K-means clustering algorithm is exploited to further improve the efficiency of execution. In the end, security analysis and extensive experiments demonstrate the high performance of EFSS compared with existing schemes.