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

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 analys...

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Autores principales: Tang, Haixu, Jiang, Xiaoqian, Wang, Xiaofeng, Wang, Shuang, Sofia, Heidi, Fox, Dov, Lauter, Kristin, Malin, Bradley, Telenti, Amalio, Xiong, Li, Ohno-Machado, Lucila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5062944/
https://www.ncbi.nlm.nih.gov/pubmed/27733153
http://dx.doi.org/10.1186/s12920-016-0224-3
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author Tang, Haixu
Jiang, Xiaoqian
Wang, Xiaofeng
Wang, Shuang
Sofia, Heidi
Fox, Dov
Lauter, Kristin
Malin, Bradley
Telenti, Amalio
Xiong, Li
Ohno-Machado, Lucila
author_facet Tang, Haixu
Jiang, Xiaoqian
Wang, Xiaofeng
Wang, Shuang
Sofia, Heidi
Fox, Dov
Lauter, Kristin
Malin, Bradley
Telenti, Amalio
Xiong, Li
Ohno-Machado, Lucila
author_sort Tang, Haixu
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-016-0224-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-50629442016-10-24 Protecting genomic data analytics in the cloud: state of the art and opportunities Tang, Haixu Jiang, Xiaoqian Wang, Xiaofeng Wang, Shuang Sofia, Heidi Fox, Dov Lauter, Kristin Malin, Bradley Telenti, Amalio Xiong, Li Ohno-Machado, Lucila BMC Med Genomics Technical Advance 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-016-0224-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-13 /pmc/articles/PMC5062944/ /pubmed/27733153 http://dx.doi.org/10.1186/s12920-016-0224-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Tang, Haixu
Jiang, Xiaoqian
Wang, Xiaofeng
Wang, Shuang
Sofia, Heidi
Fox, Dov
Lauter, Kristin
Malin, Bradley
Telenti, Amalio
Xiong, Li
Ohno-Machado, Lucila
Protecting genomic data analytics in the cloud: state of the art and opportunities
title Protecting genomic data analytics in the cloud: state of the art and opportunities
title_full Protecting genomic data analytics in the cloud: state of the art and opportunities
title_fullStr Protecting genomic data analytics in the cloud: state of the art and opportunities
title_full_unstemmed Protecting genomic data analytics in the cloud: state of the art and opportunities
title_short Protecting genomic data analytics in the cloud: state of the art and opportunities
title_sort protecting genomic data analytics in the cloud: state of the art and opportunities
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5062944/
https://www.ncbi.nlm.nih.gov/pubmed/27733153
http://dx.doi.org/10.1186/s12920-016-0224-3
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