  Recent studies demonstrate that effective healthcare can benefit from using
the human genomic information. For instance, analysis of tumor genomes has
revealed 140 genes whose mutations contribute to cancer. As a result, 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 or relationships between genetic variants and
diseases. In this case, 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 sensitivity and privacy
concerns regarding the genomic data, multi-jurisdictional laws and policies of
cross-border genomic data sharing are enforced among different regions of the
world. 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 (Intel SGX) 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 or experimented to this date. Our proposed framework, SAFETY is up
to 4.82 times faster than the best existing secure computation technique.
