Privacy-Preserving Search for a Similar Genomic Makeup in the Cloud

Abstract

In this paper, we attempt to provide a privacy-preserving and efficient solution for the "similar genomic makeup search'' problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we propose a hierarchical index structure to index each hospital's dataset with low memory requirements. Furthermore, we develop a novel privacy-preserving index merging mechanism that merges individual indices into a common index and significantly improves search efficiency. We also consider the storage of medical information associated with the genomic data of a patient. We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. We conduct experiments on large-scale genomic data and show the high efficiency of the proposed scheme.