Achieving Privacy-Preserving Edit Distance Query in Cloud and Its Application to Genomic Data
Jason S. Chang, Rongxing Lu · 2019
Edit distance is one of the most frequently used metrics for sequence comparison in many fields, including genomic data computation. With growing availability of cloud computing, data owners tend to outsource to cloud servers for edit distance computation. However, directly outsourcing and computing edit distance on the cloud inevitably raises concerns over privacy. Therefore, in this paper, we propose a secure and efficient one-server model for achieving privacy-preserving edit distance queries in the cloud. The proposed scheme combines BGN encryption and improved data generation technique to securely and efficiently process queries without revealing the original data to the cloud. In addition, we modify our scheme with a block-wise edit distance approximation for privacy-preserving genomic data comparison. A detailed security analysis proves that our proposed scheme is secure and privacy preserving and performance evaluations indicate the efficiency of the scheme.