Efficient and Privacy-Preserving Edit Distance Query over Encrypted Genomic Data

Genomic data have seen tremendous growth recently due to the advancement in DNA sequencing technologies and are being generated at an ever-higher velocity. The similarity between two genomic data records can be measured by the edit distance, which has significant applications in disease diagnosis and treatment. At the same time, due to the limited storage and computational resources at the local data owner, a typical solution for the edit distance computation is to outsource the genomic data to a powerful cloud. However, as the genomic data are sensitive and the cloud server is not fully trusted, there are privacy considerations during the edit distance computation. Apart from data privacy, efficiency also needs to be taken into consideration. In order to deal with the privacy and efficiency issues, in this paper, we propose an efficient and privacy-preserving edit distance computation scheme for a single data owner and single cloud server scenario. In specific, we first design an index technique to reduce the computational cost and communication overhead of the genomic data outsourcing, and introduce a fast edit distance computation technique to speed up the edit distance query. Then, we propose an efficient and privacy-preserving edit distance computation scheme by deploying the homomorphic encryption technique, which can well preserve the private information including genomic data records and edit distances. Besides, security analysis shows that the proposed scheme is privacy-preserving and performance evaluation validates the efficiency of the proposed scheme.