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RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19

RAPID：SaTC：事实：基于联合分析的 COVID-19 接触者追踪

基本信息

批准号：
2031799
负责人：
Lalitha Sankar
金额：
$ 20万
依托单位：
Arizona State University
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：
2020-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031799&HistoricalAwards=false
关键词：
RAPID SaTC FACT Federated Analytics

项目摘要

The spread of the Corona Virus Disease 2019 (COVID-19), a highly-infectious disease caused by a newly discovered coronavirus, has reached pandemic levels across the globe. As the numbers in the USA of infections, critical care interventions and deaths continue to rise, mobile applications (apps) that enable contact tracing (CT) are being rapidly deployed to monitor the spread of COVID-19. However, on-going deployments, based on anonymously sharing tokens without exploiting the rich local device data, are insufficient in monitoring disease spread in a timely manner and are also vulnerable to privacy and security attacks. There is an urgent need to develop CT apps that not only monitor but also intervene to limit COVID-19 spread while respecting user security and privacy. This project addresses this challenge via Federated Analytics based Contact Tracing (FACT), a refined federated learning approach to leverage both device-level data and server capabilities in a private and secure manner. FACT enables prevention and intervention by including hotspot identification, user alerts, and continual assessment of user COVID-19 risk. FACT guarantees a private and secure way to (i) evaluate a user's need for testing or their resilience to exposure, and (ii) assess herd immunity across the population. FACT addresses both the vulnerability of current Bluetooth-based systems to a variety of attacks and limited learning at the server by proposing a secure GPS+Bluetooth system which will enable the server to detect geographical infection clusters in a privacy-preserving manner. FACT also harnesses the rich device-level mobility and acoustic sensing data to periodically predict risks of those exposed to COVID-19 positive patients using federated learning and without sharing any device data with the server. Several simple, but well-validated, parameters are extracted from these sensors to develop local digital markers of COVID risk. At the heart of these innovations are the refinements FACT brings to standard federated learning via knowledge distillation and model compression. This project, with the support of ASU University Technology Office and collaboration with industry companies, will deploy and evaluate FACT via a mobile app and reach many users. FACT can extend the clinical utility of acoustic measures, adopted in general clinical trials, for use in COVID-19 patients. This research also provides immense opportunities to train and expose diverse graduate students to the technical challenges of ensuring privacy and security while simultaneously enabling socially beneficial technologies.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.

2019冠状病毒病（COVID-19）是一种由新发现的冠状病毒引起的高度传染性疾病，在全球范围内的传播已达到大流行水平。随着美国感染人数、重症监护干预措施和死亡人数持续上升，支持接触者追踪（CT）的移动应用程序正在迅速部署，以监测COVID-19的传播。然而，基于匿名共享令牌而不利用丰富的本地设备数据的正在进行的部署不足以及时监测疾病传播，并且容易受到隐私和安全攻击。在尊重用户安全和隐私的同时，迫切需要开发既能监测又能干预的CT应用程序，以限制新冠病毒的传播。该项目通过基于接触跟踪（FACT）的联邦分析解决了这一挑战，这是一种改进的联邦学习方法，以私有和安全的方式利用设备级数据和服务器功能。FACT通过热点识别、用户警报和持续评估用户COVID-19风险，实现预防和干预。FACT保证以一种私密和安全的方式(i)评估用户对检测的需求或其对暴露的适应能力，以及（ii）评估整个人群的群体免疫力。FACT通过提出一种安全的GPS+蓝牙系统，使服务器能够以保护隐私的方式检测地理感染集群，解决了当前基于蓝牙系统对各种攻击的脆弱性和服务器上有限的学习能力。FACT还利用丰富的设备级移动性和声学传感数据，通过联合学习定期预测接触COVID-19阳性患者的风险，而无需与服务器共享任何设备数据。从这些传感器中提取几个简单但经过充分验证的参数，以开发COVID风险的本地数字标记。这些创新的核心是FACT通过知识蒸馏和模型压缩为标准联邦学习带来的改进。在亚利桑那州立大学技术办公室的支持下，该项目将与行业公司合作，通过移动应用程序部署和评估FACT，并接触到许多用户。FACT可以扩展一般临床试验中采用的声学测量的临床应用，用于COVID-19患者。这项研究还提供了巨大的机会来培训和暴露不同的研究生，以确保隐私和安全的技术挑战，同时实现对社会有益的技术。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。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer

通过过滤器修剪和知识转移在边缘设备上启用深度学习

DOI：
10.48550/arxiv.2201.10947
发表时间：
2022
期刊：
arXiv:2201.10947 [cs.LG]
影响因子：
0
作者：
Kaiqi Zhao, Yitao Chen
通讯作者：
Kaiqi Zhao, Yitao Chen

Catalic: Delegated PSI Cardinality with Applications to Contact Tracing

Catalic：委托 PSI 基数及其在联系人追踪中的应用

DOI：
10.1007/978-3-030-64840-4_29
发表时间：
2021
期刊：
International Conference on the Theory and Application of Cryptology and Information Security
影响因子：
0
作者：
Duong, Thai;Phan, Hieu;Trieu, Ni.
通讯作者：
Trieu, Ni.

Being Properly Improper