课题基金 / 基金详情

NSF Convergence Accelerator: Privacy and Pandemics: Responsible Use of Data During Times of Crisis

NSF Convergence Accelerator: Privacy and Pandemics: Responsible Use of Data During Times of Crisis
NSF 融合加速器:隐私与流行病:危机期间负责任地使用数据
批准号:
2035358
负责人:
Jules Polonetsky
金额:
$9.61万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2021-02-28

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中文摘要
翻译
NSF融合加速器支持以团队为基础的多学科努力,解决国家重要性的挑战,并在不久的将来显示出可交付成果的潜力。本次研讨会的目的是探讨2021财年国家科学基金会融合加速器的潜在主题。隐私的未来论坛(FPF)将为流行病和其他危机情况召开一次关于“危机期间负责任地使用数据”的讲习班。该研讨会将汇集美国政府领导人、国际数据保护当局、企业领导人、技术专家、学术研究人员和公共卫生专家,探讨在危机期间(包括COVID-19危机期间)收集和保护数据以支持公共卫生举措的利益、风险和战略。确保领导应急工作的美国和全球利益攸关方拥有以数据为基础的知识、工具和治理结构,以应对大流行病的挑战,是我们这个时代的决定性公共政策问题之一。本次讲习班将探讨拟议的“趋同加速器”轨道,以加速政府、工业界和学术研究人员之间的协作,使卫生数据变得有效和可用,并对今后防范大流行病和其他危机局势产生积极和持久的影响。该项目的目标有三个方面:1)将专业知识带给决策者和行业领导者的不同受众,以便及时考虑、合作和适当应用;2)解决一系列顶级问题和技术中的一些优先研究问题;3)确定挑战、信息差距和研究与开发的前景领域。C-Accel研讨会将以“虚拟会议”的形式进行,旨在让参与者参与沉浸式讨论和审议来自顶级COVID的主题/发现,以及FPF组织的隐私与流行病教育系列中出现的数据相关问题。三个讨论支柱将被用作组织主题:人工智能-伦理-健康。该项目将制定一份协调一致的全球“路线图”(白皮书),为研究方向、实践改进和隐私保护产品和服务的开发指明前进方向,为COVID-19和危机形势政策提供信息,并为未来的大流行和其他危机做好准备。该奖项反映了美国国家科学基金会的法定使命,并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,被认为值得支持。
英文摘要
The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The objective of this workshop is to explore topics for potential NSF Convergence Accelerator tracks for FY 2021.The Future of Privacy Forum (FPF) will convene a workshop on “The Responsible Use of Data During Times of Crisis”, for pandemics as well as other crisis situations. The workshop will bring together US government leaders, international data protection authorities, corporate leaders, technologists, academic researchers and public health experts to examine benefits, risks, and strategies for the collection and protection of data in support of public health initiatives during crises, including for COVID-19. Ensuring that US and global stakeholders leading emergency efforts have the data-based knowledge, tools and governance structures to navigate pandemic challenges is one of the defining public policy issues of our time. This workshop will explore a proposed Convergence Accelerator track to accelerate collaboration among government, industry and academic researchers to make health data effective and usable and achieve positive lasting impact on future preparedness for pandemics and other crisis situations.The objectives of the project are three-fold: 1) to bring expert knowledge to a diverse audience of policymakers and industry leaders for timely consideration, collaboration and application as appropriate, 2) to address a number of priority research questions across a range of top issues and technologies, and 3) identify challenges, information gaps, and prospective areas for research and development. The C-Accel workshop will proceed as a “virtual conference” designed to engage participants in immersive discussion and consideration of topics/findings from top COVID and data-related issues that emerged from the Privacy & Pandemics educational series organized by FPF. Three discussion pillars will be used as organizing themes: AI-Ethics-Health. The project will produce a coordinated global “roapmap” (white paper) to point the way forward in research directions, practice improvements, and development of privacy-preserving products and services to inform COVID-19 and crisis situation policies and in preparation for future pandemics and other crises.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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会议论文
NSF Convergence Accelerator: The Future of Privacy Technology-Inaugural Conference of the Privacy Tech Alliance
Spokes: SMALL: SOUTH: Smart Privacy for Smart Cities: A Research Collaborative to Protect Privacy and Use Data Responsibly
RCN: Applied Privacy Research Coordination Network: An Industry-Academic Network to Transition Promising Privacy Research to Practice
Privacy Research and Data Responsibility Research Coordination Network
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