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Protecting genomic data analytics in the cloud: state of the art and opportunities | BMC Medical Genomics | Full Text

Protecting genomic data analytics in the cloud: state of the art and opportunities | BMC Medical Genomics | Full Text
Biomed Central
BMC Medical Genomics

Protecting genomic data analytics in the cloud: state of the art and opportunities

  • Haixu TangEmail author,
  • Xiaoqian JiangView ORCID ID profile,
  • Xiaofeng Wang,
  • Shuang Wang,
  • Heidi Sofia,
  • Dov Fox,
  • Kristin Lauter,
  • Bradley Malin,
  • Amalio Telenti,
  • Li Xiong and
  • Lucila Ohno-Machado
BMC Medical GenomicsBMC series – open, inclusive and trusted20169:63
DOI: 10.1186/s12920-016-0224-3
Received: 4 April 2016
Accepted: 28 September 2016
Published: 13 October 2016


The outsourcing of genomic data into public cloud computing settings raises concerns over privacy and security. Significant advancements in secure computation methods have emerged over the past several years, but such techniques need to be rigorously evaluated for their ability to support the analysis of human genomic data in an efficient and cost-effective manner. With respect to public cloud environments, there are concerns about the inadvertent exposure of human genomic data to unauthorized users. In analyses involving multiple institutions, there is additional concern about data being used beyond agreed research scope and being prcoessed in untrused computational environments, which may not satisfy institutional policies. To systematically investigate these issues, the NIH-funded National Center for Biomedical Computing iDASH (integrating Data for Analysis, ‘anonymization’ and SHaring) hosted the second Critical Assessment of Data Privacy and Protection competition to assess the capacity of cryptographic technologies for protecting computation over human genomes in the cloud and promoting cross-institutional collaboration. Data scientists were challenged to design and engineer practical algorithms for secure outsourcing of genome computation tasks in working software, whereby analyses are performed only on encrypted data. They were also challenged to develop approaches to enable secure collaboration on data from genomic studies generated by multiple organizations (e.g., medical centers) to jointly compute aggregate statistics without sharing individual-level records. The results of the competition indicated that secure computation techniques can enable comparative analysis of human genomes, but greater efficiency (in terms of compute time and memory utilization) are needed before they are sufficiently practical for real world environments.

Author summary

Advancement of technology significantly reduces the price of obtaining whole genome sequencing (WGS) data and makes personalized genome analysis more affordable. The increasing availability of human genomic data is accompanied with increasing privacy concerns, such that the inappropriate disclosure of such data might put individuals at risk. In this paper, we present the recent findings of novel genomic data protection methods through a community-wide open competition to address the emerging privacy challenges. The goal of the competition is to bridge the gap between the biomedical informatics, data privacy, and security communities.

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