Xiaojie Zhu

Xiaojie Zhu, Erman Ayday, Roman Vitenberg

With the increasing affordability and availability of patient data, hospitals tend to outsource their data to cloud service providers (CSPs) for the purpose of storage and analytics. However, the concern of data privacy significantly limits the data owners' choice. In this work, we propose the first solution, to the best of our knowledge, that allows a CSP to perform efficient identification of target patients (e.g., pre-processing for a genome-wide association study - GWAS) over multi-tenant encrypted phenotype data (owned by multiple hospitals or data owners). We first propose an encryption mechanism for phenotype data, where each data owner is allowed to encrypt its data with a unique secret key. Moreover, the ciphertext supports privacy-preserving search and, consequently, enables the selection of the target group of patients (e.g., case and control groups). In addition, we provide a per-query based authorization mechanism for a client to access and operate on the data stored at the CSP. Based on the identified patients, the proposed scheme can either (i) directly conduct GWAS (i.e., computation of statistics about genomic variants) at the CSP or (ii) provide the identified groups to the client to directly query the corresponding data owners and conduct GWAS using existing distributed solutions. We implement the proposed scheme and run experiments over a real-life genomic dataset to show its effectiveness. The result shows that the proposed solution is capable to efficiently identify the case/control groups in a privacy-preserving way.

Xiaojie Zhu, Erman Ayday, Roman Vitenberg et al.

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 $95$ times faster than Asharov et al.'s solution.