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CICI:TCR: Enhancing Security and Privacy of Community Cyberinfrastructures for Collaborative Research

CICI:TCR: Enhancing Security and Privacy of Community Cyberinfrastructures for Collaborative Research
CICI:TCR:增强社区网络基础设施的安全性和隐私性以进行协作研究
批准号:
2319988
负责人:
Haiying Shen
金额:
$120.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2026-07-31

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中文摘要
翻译
网络基础设施(CI)系统,包括高性能计算和数据中心,在支持学术机构的研究方面发挥着关键作用。随着科学工作变得更加复杂和协作,有必要加强CI的安全和隐私方面,以应对不断变化的威胁。该项目利用现有的CI系统作为开发平台,并整合新的网络安全研究,以创建更安全和更具弹性的CI环境。具体地说,该项目的重点是开发先进的技术,以便在CI系统内进行安全的数据管理和计算。它有三个推力。在《推力1:在集中计算中保护数据》一书中,采用了尖端技术来保护集中计算环境中数据的安全和隐私。在《推力2:在分布式计算中保护数据》中,部署了最先进的技术来保护协作计算场景中数据的安全和隐私。在推力3:保护数据计算中,开发了创新的统计和基于机器学习的方案来检测和缓解内存拒绝服务攻击,确保计算的安全性。通过实施最先进的安全协议和增强隐私的技术,该项目旨在增强CI系统对不断变化的网络威胁的应变能力。该项目包括网络安全研究的技术过渡,授权CI,并在科学和工程领域实现高度安全和值得信赖的应用程序。它推动了CI安全的最先进水平,为尖端发现和创新铺平了道路,同时确保了科学数据和计算的安全。此外,它还促进了网络安全领域的进一步研究、试验和进步,并有助于增强国家CI生态系统的安全和隐私保护能力。该项目为学生提供全面的培训机会,为研究生和本科生提供合作研究前景,教师参与,以及K-12学生的参与。NSF特别注意从代表性不足的群体中招收学生,以培养包容性和多样性。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。
英文摘要
Cyberinfrastructure (CI) systems, including high-performance computing and data centers, play a critical role in supporting research in academic institutions. As scientific endeavors become more complex and collaborative, it is necessary to enhance CI’s security and privacy aspects to address evolving threats. This project leverages an existing CI system as a development platform and integrate novel cybersecurity research to create a more secure and resilient CI environment. Specifically, the project focuses on developing advanced techniques for secure data management and computation within CI systems. It has three thrusts. In thrust 1: Protect Data in Centralized Computing, cutting-edge techniques are employed to safeguard the security and privacy of data in centralized computing environments. In thrust 2: Protect Data in Distributed Computing, state-of-the-art techniques are deployed to protect the security and privacy of data in collaborative computing scenarios. In thrust 3: Protect Data Computation, innovative statistical and machine learning-based schemes are developed to detect and mitigate memory denial-of-service attacks, ensuring the security of computation. By implementing state-of-the-art security protocols and privacy-enhancing technologies, the project aims to enhance the resilience of CI systems against evolving cyber threats.This project encompasses technology transitions in cybersecurity research, empowering CIs and enabling highly secure and trustworthy applications in scientific and engineering domains. It advances the state-of-the-art in CI security, paving the way for cutting-edge discoveries and innovations while ensuring the security of scientific data and computation. Additionally, it facilitates further research, experimentation, and advancements in the field of cybersecurity, and contributes to enhancing the security and privacy protection capabilities of the national CI ecosystem. This project provides comprehensive training opportunities for students, collaborative research prospects for graduate and undergraduate participants, faculty engagement, and involvement of K-12 students. Special attention are given to recruiting students from underrepresented groups to foster inclusivity and diversity.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.
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Efficient Astronomical Data Processing Among Distributed Astronomical Radio Observatories
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