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

DNA测序技术的进步预计将在未来几年提供大量的人类基因组数据，这些数据需要进行巨大规模的计算才能处理。今天的商业云无法满足这一需求，因为它们没有提供强有力的隐私保障，或者使用现有的加密技术，因为它们仍然无法达到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数据保护提供了新的机会，通过设计成即使在完全受损的操作系统下也能抵御攻击的处理器。为了抓住这一机会，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。来自历史上黑人学院和大学的学生将参与这项工作。该项目专注于开发一个基于英特尔软件保护扩展(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系统独特性能影响的理解，包括飞地创建、管理、信任建立、跨飞地通信等方面的影响，构建了一种新的基于MPI的集群计算框架，以在资源受限的情况下自动优化计算节点跨飞地和CPU包的部署。这个新的框架支持一系列基本的基因组计算任务，从阅读图谱到肽识别，以及基于机器学习的模型。此外，还对其潜在风险，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。这项工作将使以前无法访问的数据得以广泛共享，并有助于推动对个性化医疗保健的新见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.