RAPID: SafePaths: A privacy-first contact tracing solution for early interventions of COVID-19 spread during the first wave and to minimize the second wave of the epidemic
RAPID:SafePaths:隐私优先的接触者追踪解决方案,用于在第一波疫情期间早期干预 COVID-19 传播,并最大限度地减少第二波疫情
基本信息
- 批准号:2031288
- 负责人:
- 金额:$ 10万
- 依托单位:
- 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
- 起止时间:2020-05-15 至 2021-04-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The objective of this project is to develop and deploy a privacy-first digital solution for public health coordination including contact-tracing to curb pandemics like COVID-19 spread. The key is to provide location and context for citizens and public health experts. Current approaches operate on a trade-off between privacy and effectiveness, relying on general public broadcasting that introduces uncertainty in the information extracted or resorting to privacy-violating technologies that risk individual rights against stigmatization and surveillance. This project will break past this dichotomy by developing a technology-based solution for coordinating information on infection and possible transmission through contact-tracing while protecting the privacy rights of viral carriers and unexposed citizens.Beyond assisting the containment of COVID-19 pandemic by contact tracing, this project will make empirical contributions to the fields of computing, healthcare, crisis response, and more. With privacy preservation being the key aspect of this project, contact tracing is achieved by using encrypted GPS trails and rotating Bluetooth identifiers. In this approach, redacted information of an infected individual is only shared while no information leaves the device of a healthy person. Specifically, this project will advance knowledge regarding: 1.) how cryptographic techniques can be implemented on ubiquitous platforms like smart phones through easy to use apps to efficiently use privatized data without leakage of any sensitive information; 2) how personal-technology solutions to societal crises can effectively influence behavior and consequently affect the outcome of such crises; and 3) how “split-learning”, a resource efficient distributed AI technique can be implemented with personal information on health, demographic, travel history, spatial context, and real-world engagement to perform private risk-assessment post contact-tracing to reduce false alarm rates. The solution is being built by a consortium of epidemiologists, engineers, data scientists, digital privacy evangelists, professors and researchers from reputable institutions. This is crucial to reduce disruption in socio-economic activity and keep panic under rationally controllable levels in response to future emergencies.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.
该项目的目标是开发和部署一个隐私优先的数字解决方案,用于公共卫生协调,包括接触者追踪,以遏制COVID-19等流行病的传播。关键是为公民和公共卫生专家提供位置和背景。目前的做法是在隐私和有效性之间进行权衡,依赖于一般公共广播,这给所提取的信息带来了不确定性,或者诉诸侵犯隐私的技术,使个人权利面临遭受污名化和监视的风险。该项目将突破这一二分法,开发一种基于技术的解决方案,通过接触者追踪来协调感染和可能传播的信息,同时保护病毒携带者和未接触过病毒的公民的隐私权。除了通过接触者追踪来帮助遏制COVID-19大流行之外,该项目还将在计算、医疗保健、危机应对等领域做出经验性贡献。隐私保护是该项目的关键方面,通过使用加密的GPS轨迹和旋转蓝牙标识符来实现联系人跟踪。在这种方法中,受感染个体的编辑信息仅被共享,而没有信息离开健康人的设备。具体而言,该项目将促进以下方面的知识:1。如何通过易于使用的应用程序在智能手机等无处不在的平台上实施加密技术,以有效地使用私有化数据,而不会泄露任何敏感信息; 2)针对社会危机的个人技术解决方案如何有效地影响行为,从而影响此类危机的结果;以及3)如何利用关于健康、人口统计、旅行历史、空间背景的个人信息来实现“分裂学习”,一种资源有效的分布式AI技术,以及现实世界的参与,以在接触者追踪后执行私人风险评估,从而降低误报率。该解决方案由来自知名机构的流行病学家、工程师、数据科学家、数字隐私布道者、教授和研究人员组成的联盟构建。这对于减少社会经济活动的中断,并将恐慌保持在合理可控的水平,以应对未来的紧急情况至关重要。该奖项反映了NSF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。
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
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Ramesh Raskar其他文献
Private independence testing across two parties
两方的私人独立性测试
- DOI:
10.48550/arxiv.2207.03652 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Praneeth Vepakomma;M. Amiri;C. Canonne;Ramesh Raskar;A. Pentland - 通讯作者:
A. Pentland
DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions
DeepABM:通过几何学习框架进行可扩展且高效的基于代理的模拟 - Covid-19 传播和干预的案例研究
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Ayush Chopra;Ramesh Raskar;J. Subramanian;Balaji Krishnamurthy;E. Gel;Santiago Romero;K. Pasupathy;Thomas C. Kingsley - 通讯作者:
Thomas C. Kingsley
Computational Schlieren Photography with Light Field Probes
- DOI:
10.1007/s11263-013-0652-x - 发表时间:
2013-08-20 - 期刊:
- 影响因子:9.300
- 作者:
Gordon Wetzstein;Wolfgang Heidrich;Ramesh Raskar - 通讯作者:
Ramesh Raskar
Surface Reconstruction from Gradient Fields via Gradient Transformations
- DOI:
10.1007/s11263-009-0302-5 - 发表时间:
2009-10-08 - 期刊:
- 影响因子:9.300
- 作者:
Amit Agrawal;Ramesh Raskar;Rama Chellappa - 通讯作者:
Rama Chellappa
Ramesh Raskar的其他文献
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{{ truncateString('Ramesh Raskar', 18)}}的其他基金
Collaborative Research: Workshop to Develop a Roadmap for Greater Public Use of Privacy-Sensitive Government Data
合作研究:制定路线图以扩大公众使用隐私敏感政府数据的研讨会
- 批准号:
2129970 - 财政年份:2021
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
RAPID: Decentralization and Privacy for Secure Vaccination Coordination
RAPID:安全疫苗协调的权力下放和隐私
- 批准号:
2115149 - 财政年份:2021
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
Collaborative Research: Computational Photo-Scatterography: Unraveling Scattered Photons for Bio-Imaging
合作研究:计算光散射术:解开生物成像的散射光子
- 批准号:
1729931 - 财政年份:2018
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
RAPID: MIT in Nashik: Creating a Model for Smart Citizens
RAPID:纳西克麻省理工学院:为智慧公民创建模型
- 批准号:
1549671 - 财政年份:2015
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
RI: Small: Time Resolved Imaging: New Methods for Capture, Analysis and Applications
RI:小型:时间分辨成像:捕获、分析和应用的新方法
- 批准号:
1527181 - 财政年份:2015
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
CGV: Small: Collaborative Research: Diffractive masks and algorithms for light field capture
CGV:小型:协作研究:用于光场捕获的衍射掩模和算法
- 批准号:
1218411 - 财政年份:2012
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
I-Corps: RetiCue: Interactive Retinal Imaging for Improved Global Eye Health
I-Corps:RetiCue:交互式视网膜成像,改善全球眼睛健康
- 批准号:
1248374 - 财政年份:2012
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
CGV: Small: Inverse Light Transport Under Femto-Photography and Transient Imaging
CGV:小:飞秒摄影和瞬态成像下的逆光传输
- 批准号:
1115680 - 财政年份:2011
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
CGV: Small: Collaborative Research: AdaCID: Adaptive Coded Imaging and Displays
CGV:小型:协作研究:AdaCID:自适应编码成像和显示
- 批准号:
1116452 - 财政年份:2011
- 资助金额:
$ 10万 - 项目类别:
Standard Grant