RAPID: Collaborative: PPSRC: Privacy-Preserving Self-Reporting for COVID-19
RAPID:协作:PPSRC:COVID-19 隐私保护自我报告
基本信息
- 批准号:2034235
- 负责人:
- 金额:$ 13.31万
- 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:2020-07-01 至 2021-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
With the reopening of the country and the restarting of economic as well as other activities, it becomes important to ensure vigilantly monitoring the spreading of COVID-19 and nimbly adjusting the policies accordingly. Technologies can play an important role in this process. This project aims at understanding how mobile-app based contact tracing and symptom monitoring technologies can help in fighting COVID-19 and deploying effective technologies. It is expected that knowledge learned through the project can be a valuable tool to fight SARS-CoV-2 as well as future emerging pathogens. This project has two thrusts. Thrust 1 studies to what extent contact tracing based on mobile phones can help. Thrust 1 has three tasks. First, summarize the design choices of existing proposals, systematically explore the design space, and identify potential privacy and security attacks and possible defenses. Second, use existing data related to contact tracing to understand the epidemiological characteristics of COVID-19. Third, assess the effectiveness of technology-aided case finding and contact-tracing using simulation models. Thrust 2 aims at developing Privacy-Preserving COVID-19 Symptom Monitoring Technologies, through three tasks. First, design and implement privacy protection technologies based on local differential privacy. Second, design and implement an App for self-reporting of symptoms. Third, integrate data collected from the app with data from other public sources, and model the integrated data using a dynamic state-space model to provide early warning for high risk districts.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.
随着国家的重新开放以及经济和其他活动的重新开始,确保警惕地监测COVID-19的传播并相应地灵活调整政策变得重要。技术可以在这一过程中发挥重要作用。该项目旨在了解基于移动应用程序的接触者追踪和症状监测技术如何帮助抗击COVID-19并部署有效的技术。预计通过该项目学到的知识可以成为抗击SARS-CoV-2以及未来新出现的病原体的宝贵工具。这个项目有两个重点。推力1研究基于移动的电话的联系人追踪能在多大程度上提供帮助。第一个目标有三项任务。首先,总结现有提案的设计选择,系统地探索设计空间,并确定潜在的隐私和安全攻击以及可能的防御措施。其次,利用现有的接触者追踪相关数据,了解COVID-19的流行病学特征。第三,使用模拟模型评估技术辅助病例发现和接触者追踪的有效性。Thrust 2旨在通过三项任务开发保护隐私的COVID-19症状监测技术。首先,设计并实现了基于局部差分隐私的隐私保护技术。其次,设计并实现一个用于症状自我报告的App。第三,将从应用程序收集的数据与其他公共来源的数据进行整合,并使用动态状态空间模型对整合后的数据进行建模,为高风险地区提供早期预警。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)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ninghui Li其他文献
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols
PURE:用于分析基于接近度的接触追踪协议的框架
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:16.6
- 作者:
F. Cicala;Weicheng Wang;Tianhao Wang;Ninghui Li;E. Bertino;F. Liang;Yang Yang - 通讯作者:
Yang Yang
Fisher Information as a Utility Metric for Frequency Estimation under Local Differential Privacy
Fisher信息作为本地差分隐私下频率估计的效用度量
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Milan Lopuhaä;B. Škorić;Ninghui Li - 通讯作者:
Ninghui Li
A formal semantics for P3P
P3P 的形式化语义
- DOI:
- 发表时间:
2004 - 期刊:
- 影响因子:0
- 作者:
Ting Yu;Ninghui Li;A. Antón - 通讯作者:
A. Antón
Anonymizing Network Traces with Temporal Pseudonym Consistency
通过时间假名一致性对网络跟踪进行匿名化
- DOI:
- 发表时间:
2012 - 期刊:
- 影响因子:0
- 作者:
Wahbeh H. Qardaji;Ninghui Li - 通讯作者:
Ninghui Li
Sensornet
传感器网
- DOI:
- 发表时间:
2009 - 期刊:
- 影响因子:0
- 作者:
Rodney Topor;Kenneth Salem;Amarnath Gupta;K. Goda;John F. Gehrke;N. Palmer;Mohamed Sharaf;Alexandros Labrinidis;J. Roddick;Ariel Fuxman;Renée J. Miller;Wang;Anastasios Kementsietsidis;Philippe Bonnet;D. Shasha;Ronald Peikert;Bertram Ludäscher;S. Bowers;T. McPhillips;Harald Naumann;K. Voruganti;J. Domingo;Ben Carterette;Panagiotis G. Ipeirotis;Marcelo Arenas;Y. Manolopoulos;Y. Theodoridis;V. Tsotras;B. Carminati;Jan Jurjens;Eduardo B. Fernandez;Murat Kantarcıoǧlu;Jaideep Vaidya;Indrakshi Ray;Athena Vakali;Cristina Sirangelo;E. Pitoura;Himanshu Gupta;Surajit Chaudhuri;G. Weikum;Ulf Leser;David W. Embley;Fausto Giunchiglia;P. Shvaiko;Mikalai Yatskevich;Edward Y. Chang;Christine Parent;S. Spaccapietra;E. Zimányi;G. Anadiotis;S. Kotoulas;Ronny Siebes;Grigoris Antoniou;D. Plexousakis;J. Bailey;François Bry;Tim Furche;Sebastian Schaffert;David Martin;Gregory D. Speegle;Krithi Ramamritham;P. Chrysanthis;Kai;Stéphane Bressan;S. Abiteboul;D. Suciu;G. Dobbie;Tok Wang Ling;Sugato Basu;Ramesh Govindan;Michael H. Böhlen;C. S. Jensen;Jianyong Wang;K. Vidyasankar;A. Chan;Serge Mankovski;S. Elnikety;P. Valduriez;Yannis Velegrakis;Mario A. Nascimento;Michael Huggett;Andrew U. Frank;Yanchun Zhang;Guandong Xu;R. Snodgrass;Alan Fekete;Marcus Herzog;Konstantinos Morfonios;Y. Ioannidis;E. Wohlstadter;M. Matera;F. Schwagereit;Steffen Staab;Keir Fraser;Jingren Zhou;M. Mokbel;Walid G. Aref;Mirella M. Moro;Markus Schneider;Panos Kalnis;Gabriel Ghinita;Michael F. Goodchild;Shashi Shekhar;James Kang;Vijayaprasath Gandhi;Nikos Mamoulis;Betsy George;Michel Scholl;Agnès Voisard;Ralf Hartmut Güting;Yufei Tao;Dimitris Papadias;Peter Revesz;G. Kollios;E. Frentzos;Apostolos N. Papadopoulos;Bernhard Thalheim;Jovan Pehcevski;Benjamin Piwowarski;S. Theodoridis;Konstantinos Koutroumbas;George Karabatis;Don Chamberlin;Philip A. Bernstein;Michael H. Böhlen;J. Gamper;Ping Li;Kazimierz Subieta;S. Harizopoulos;Ethan Zhang;Yi Zhang;Theodore Johnson;Hans;S. Fienberg;Jiashun Jin;Radu Sion;C. Paice;Nikos Hardavellas;Ippokratis Pandis;Edie M. Rasmussen;Hiroshi Yoshida;G. Graefe;Bernd Reiner;Karl Hahn;K. Wada;T. Risch;Jiawei Han;Bolin Ding;Lukasz Golab;Michael Stonebraker;Bibudh Lahiri;Srikanta Tirthapura;Erik Vee;Yanif Ahmad;U. Çetintemel;Mitch Cherniack;S. Zdonik;Mariano P. Consens;M. Lalmas;R. Baeza;D. Hiemstra;Peer Krögerand;Arthur Zimek;Nick Craswell;Carson Kai;Maxime Crochemore;Thierry Lecroq;Arie Shoshani;Jimmy Lin;Hwanjo Yu;David B. Lomet;H. Hinterberger;Ninghui Li;Phillip B. Gibbons;Mouna Kacimi;Thomas Neumann - 通讯作者:
Thomas Neumann
Ninghui Li的其他文献
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{{ truncateString('Ninghui Li', 18)}}的其他基金
Collaborative Research: SaTC: CORE: Small: Differentially Private Data Synthesis: Practical Algorithms and Statistical Foundations
协作研究:SaTC:核心:小型:差分隐私数据合成:实用算法和统计基础
- 批准号:
2247794 - 财政年份:2023
- 资助金额:
$ 13.31万 - 项目类别:
Continuing Grant
Collaborative Proposal: SaTC: Frontiers: Center for Distributed Confidential Computing (CDCC)
协作提案:SaTC:前沿:分布式机密计算中心 (CDCC)
- 批准号:
2207204 - 财政年份:2022
- 资助金额:
$ 13.31万 - 项目类别:
Continuing Grant
SaTC: CORE: Medium: Collaborative: User-Centered Deployment of Differential Privacy
SaTC:核心:媒介:协作:以用户为中心的差异隐私部署
- 批准号:
1931443 - 财政年份:2020
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
SaTC: CORE: Improving Password Ecosystem: A Holistic Approach
SaTC:核心:改进密码生态系统:整体方法
- 批准号:
1704587 - 财政年份:2017
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
EAGER: Bridging The Gap between Theory and Practice in Data Privacy
EAGER:弥合数据隐私理论与实践之间的差距
- 批准号:
1640374 - 财政年份:2016
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
TWC SBE: Medium: Collaborative: User-Centric Risk Communication and Control on Mobile Devices
TWC SBE:媒介:协作:移动设备上以用户为中心的风险沟通和控制
- 批准号:
1314688 - 财政年份:2013
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
TC: Small: Provably Private Microdata Publishing
TC:小型:可证明的私人微数据出版
- 批准号:
1116991 - 财政年份:2011
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
CCS Workshops Organization Supplement
CCS 研讨会组织补充
- 批准号:
1054001 - 财政年份:2010
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
TC:Medium: Collaborative Research: Towards Formal, Risk Aware Authorization
TC:中:协作研究:迈向正式的、具有风险意识的授权
- 批准号:
0963715 - 财政年份:2010
- 资助金额:
$ 13.31万 - 项目类别:
Continuing Grant
TC:Medium:Collaborative Research:Techniques to Retrofit Legacy Code
TC:中:协作研究:改造遗留代码的技术
- 批准号:
0905442 - 财政年份:2009
- 资助金额:
$ 13.31万 - 项目类别:
Standard Grant
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