Large-Scale Privacy-Preserving Mapping of Human Genomic Sequences on Hybrid Clouds

An operation preceding most human DNA analyses is read mapping, which aligns millions of short sequences (called reads) to a reference genome. This step involves an enormous amount of computation (evaluating edit distances for millions upon billions of sequence pairs) and thus needs to be outsourced to low-cost commercial clouds. This asks for scalable techniques to protect sensitive DNA information, a demand that cannot be met by any existing techniques (e.g., homomorphic encryption, secure multiparty computation). In this paper, we report a new step towards secure and scalable read mapping on the hybrid cloud, which includes both the public commercial cloud and the private cloud within an organization. Inspired by the famous “seed-and-extend” method, our approach strategically splits a mapping task: the public cloud seeks exact matches between the keyed hash values of short read substrings (called seeds) and those of reference sequences to roughly position reads on the genome; the private cloud extends the seeds from these positions to find right alignments. Our novel seed-combination technique further moves most workload of this task to the public cloud. The new approach is found to work effectively against known inference attacks, and also easily scale to millions of reads.