In this paper, we introduce EPISODE – a highly efficient privacy-preserving protocol for Similar Sequence Queries (SSQs), which can be used for finding genetically similar individuals in an outsourced genomic database, i.e., securely aggregated from data of multiple institutions. Our SSQ protocol is based on the edit distance approximation by Asharov et al. (PETS’18), which we further optimize and extend to the outsourcing scenario. We improve their protocol by using more efficient building blocks and achieve a 5-6x run-time improvement compared to their work in the same two-party scenario.
Recently, Cheng et al. (ASIACCS’18) introduced protocols for outsourced SSQs that rely on homomorphic encryption. Our new protocol outperforms theirs by more than factor 24000x in terms of run-time in the same setting and guarantees the same level of security. In addition, we show that our algorithm scales for practical database sizes by querying a database that contains up to a million short sequences within a few minutes, and a database with hundreds of whole-genome sequences containing 75 million alleles each within a few hours.