A hybrid cloud read aligner based on MinHash and kmer voting that preserves privacy

Abstract

Low-cost clouds can alleviate the compute and storage burden of the genome sequencing data explosion. However, moving personal genome data analysis to the cloud can raise serious privacy concerns. Here, we devise a method named Balaur, a privacy preserving read mapper for hybrid clouds based on locality sensitive hashing and kmer voting. Balaur can securely outsource a substantial fraction of the computation to the public cloud, while being highly competitive in accuracy and speed with non-private state-of-the-art read aligners on short read data. We also show that the method is significantly faster than the state of the art in long read mapping. Therefore, Balaur can enable institutions handling massive genomic data sets to shift part of their analysis to the cloud without sacrificing accuracy or exposing sensitive information to an untrusted third party.