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BIGDATA: IA: Enabling Large-Scale, Privacy-Preserving Genomic Computing with a Hardware-Assisted Secure Big-Data Analytics Framework

BIGDATA：IA：利用硬件辅助的安全大数据分析框架实现大规模、隐私保护的基因组计算

基本信息

批准号：
1838083
负责人：
XiaoFeng Wang
金额：
$ 100万
依托单位：
Indiana University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838083&HistoricalAwards=false
关键词：
BIGDATA IA Enabling Large Scale

项目摘要

Advances in DNA sequencing technologies are expected to make available a massive amount of human genomic data in the years to come, which need computation of a tremendous scale to process. This demand cannot be met by today's commercial clouds, since they do not provide strong privacy guarantees, or by using existing cryptography techniques, since they still cannot achieve the performance required for big-data analytics. The emergence of the new-generation hardware support for trusted execution environments presents a new opportunity for scalable data protection, through the processors designed to withstand the attacks even from a fully compromised operating system. To seize this opportunity, this project aims at developing a distributed, parallel computing framework critical for executing data-intensive tasks on trusted execution environment-capable systems. This research will be performed in collaboration with Intel, which will transfer the new methods developed for large-scale data protection to industry and genomic researchers. Students from historically black colleges and universities will participate in the work.The project focuses on developing a big-data analytics framework built on Intel Software Guard Extensions (SGX) and applying it to support privacy-preserving, large-scale genomic data analyses and other computing tasks. Based upon the understanding of unique performance impacts of SGX systems, including those incurred by enclave creation, management, trust establishment, cross-enclave communication and others, a new MPI-based cluster computing framework is built to automatically optimize the deployment of computing nodes across enclaves and CPU packages under resource constraints. This new framework supports a set of fundamental genomic computing tasks, ranging from reads-mapping to peptide identification, as well as machine-learning based models. Also, its potential risks, side-channel leaks in particular, are analyzed and effectively controlled to provide high privacy assurance. The work will enable broad sharing of previously inaccessible data and help drive the new insights of individualized health care.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

DNA测序技术的进步有望在未来几年内提供大量的人类基因组数据，这些数据需要大规模的计算来处理。今天的商业云无法满足这一需求，因为它们不能提供强大的隐私保证，或者使用现有的加密技术，因为它们仍然无法实现大数据分析所需的性能。对可信执行环境的新一代硬件支持的出现为可扩展的数据保护提供了新的机会，通过设计用于抵御甚至来自完全受损的操作系统的攻击的处理器。为了抓住这个机会，该项目旨在开发一个分布式并行计算框架，该框架对于在具有可信执行环境能力的系统上执行数据密集型任务至关重要。这项研究将与英特尔合作进行，英特尔将把为大规模数据保护开发的新方法转移给行业和基因组研究人员。该项目的重点是开发基于英特尔Software Guard Extensions（SGX）的大数据分析框架，并将其应用于支持隐私保护、大规模基因组数据分析和其他计算任务。基于对SGX系统的独特性能影响的理解，包括由飞地创建、管理、信任建立、跨飞地通信等引起的性能影响，构建了一种新的基于MPI的集群计算框架，以在资源约束下自动优化计算节点跨飞地和CPU包的部署。这个新框架支持一系列基本的基因组计算任务，从读数映射到肽识别，以及基于机器学习的模型。此外，其潜在的风险，特别是侧通道泄漏，进行了分析和有效控制，以提供高隐私保证。这项工作将能够广泛共享以前无法访问的数据，并有助于推动个性化医疗保健的新见解。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。