Cloud-Assisted Read Alignment and Privacy

Abstract

Thanks to the rapid advances in sequencing technologies, genomic data is now being produced at an unprecedented rate. To adapt to this growth, several algorithms and paradigm shifts have been proposed to increase the throughput of the classical DNA workflow, e.g. by relying on the cloud to perform CPU intensive operations. However, the scientific community raised an alarm due to the possible privacy-related attacks that can be executed on genomic data. In this paper we review the state of the art in cloud-based alignment algorithms that have been developed for performance. We then present several privacy-preserving mechanisms that have been, or could be, used to align reads at an incremental performance cost. We finally argue for the use of risk analysis throughout the DNA workflow, to strike a balance between performance and protection of data.

Security, privacy, and trust management in DNA computing

“…However, although public clouds provide powerful computational resources at an affordable cost, they are managed by a third party, i.e., a Cloud Service Provider (CSP). Using public cloud therefore raises new challenges in order to keep genomic data secure [4]. Furthermore, the increasing availability of genomic data and its large array of potential applications encouraged data sharing to accelerate the understanding of DNA functioning, and allow the largest number to benefit from the information it encodes.…”