SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution

Abstract

Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. Consequently, many institutions are using statistical analysis of genomic data, which are mostly based on genome-wide association studies (GWAS). GWAS analyze genome sequence variations in order to identify genetic risk factors for diseases. These studies often require pooling data from different sources together in order to unravel statistical patterns, relationships between genetic variants and diseases. Here, the primary challenge is to fulfill one major objective: accessing multiple genomic data repositories for collaborative research in a privacy-preserving manner. Due to the privacy concerns regarding the genomic data, multi-jurisdictional laws and policies of cross-border genomic data sharing are enforced among different countries. In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions to ensure high efficiency and privacy at the same time. Different experimental settings show the efficacy and applicability of such hybrid framework in secure conduction of GWAS. To the best of our knowledge, this hybrid use of homomorphic encryption along with Intel SGX is not proposed to this date. SAFETY is up to 4.82 times faster than the best existing secure computation technique.

Privacy-Preserving Genome-Wide Association Study for Rare Mutations - A Secure FrameWork for Externalized Statistical Analysis

“…Data are encrypted with AES before being sent to SGX, where data are decrypted before being securely processed. In [24], authors propose a hybrid framework where several algorithms used in GWAS such as Linkage Disequilibrium (LD) computation, Hardy-Weinberg Equilibrium (HWE) test, CATT and Fisher's Exact Test (FET) can be securely performed on federated genomic datasets. They exploit homomorphic encryption and SGX due to the fact that HE allows to compute linear operation over encrypted data in a secure way, especially, the sum of all entities frequencies tables in secure way.…”

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

“…Only the core algorithms are running inside the enclave, thereby minimizing the performance overhead. With a combination of HE and SGX techniques, Sadat et al [81] presented a solution for secure genome-wide association studies (GWAS). The data is encrypted using Paillier cryptosystem [82] and then put into statistical tests within a secure enclave.…”

On Distributed Collaboration for Biomedical Analyses

“…Combinations of homomorphic encryption and SGX technology can be used, for example, when the encryption scheme is partially homomorphic but the analysis requires operations on data that cannot be performed in the encrypted domain. For example, Fisher's exact statistical test can be securely processed inside an SGX enclave after contingency tables of a single nucleotide polymorphism (SNP) from several contributors were added to each other using Paillier's additive homomorphic cryptosystem at the untrusted server level [18].…”