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

12/04/2019
by   Xiaojie Zhu, et al.
0

In this paper, we attempt to provide a privacy-preserving and efficient solution for the "similar patient 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 let each hospital encrypt its dataset with its own key and outsource the storage of its dataset to a public cloud. The physician can get authorization from multiple hospitals and send a query to the cloud, which efficiently performs the search across authorized hospitals using a privacy-preserving index structure. 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 generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). 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. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the efficiency of the proposed scheme. Notably, we show that under our experimental settings, the proposed scheme is more than 60 times faster than Wang et al.'s protocol and 97 times faster than Asharov et al.'s solution.

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