  Capturing the vast amount of meaningful information encoded in the human
genome is a fascinating research problem. The outcome of these researches have
significant influences in a number of health related fields --- personalized
medicine, paternity testing and disease susceptibility testing are a few to be
named. To facilitate these types of large scale biomedical research projects,
it oftentimes requires to share genomic and clinical data collected by
disparate organizations among themselves. In that case, it is of utmost
importance to ensure that sharing, managing and analyzing the data does not
reveal the identity of the individuals who contribute their genomic samples.
The task of storage and computation on the shared data can be delegated to
third party cloud infrastructures, equipped with large storage and high
performance computation resources. Outsourcing these sensitive genomic data to
the third party cloud storage is associated with the challenges of the
potential loss, theft or misuse of the data as the server administrator cannot
be completely trusted as well as there is no guarantee that the security of the
server will not be breached. In this paper, we provide a model for secure
sharing and computation on genomic data in a semi-honest third party cloud
server. The security of the shared data is guaranteed through encryption while
making the overall computation fast and scalable enough for real-life
large-scale biomedical applications. We evaluated the efficiency of our
proposed model on a database of Single-Nucleotide Polymorphism (SNP) sequences
and experimental results demonstrate that a query of 50 SNPs in a database of
50000 records, where each record contains 300 SNPs, takes approximately 6
seconds.
