Secure Similar Sequence Query over Multi-source Genomic Data on Cloud

Abstract

Cloud computing has been shown promising in enabling various analyses over large-scale genomic data integrated across multiple data sources. However, outsourcing data to remote cloud servers raises data-privacy concerns, therefore demands secure computing measures over the data analyzing process on the untrusted cloud servers. Due to the scale of genomic dataset and the length of each genomic sequence, it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data. To address the challenges of security and efficiency, we propose a set of two-party computing protocols in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixed form</i> , which combine secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. Moreover, our scheme supports the fusion of genomic data from multiple data owners to generate a deduplicated-joint genomic dataset, therefore reduces the redundancy in the dataset. The performance improvements of our scheme are validated through extensive experiments on a commercial cloud platform over a real-world genomic dataset.