SCC-PG: JST: Privacy-enhanced data-driven health monitoring for smart and connected senior communities

SCC-PG：JST：针对智能互联老年社区的隐私增强型数据驱动健康监测

• 批准号: 1952192
• 负责人: Li Xiong
• 金额: $ 7.5万
• 依托单位: Emory 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: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952192&HistoricalAwards=false
• 关键词: SCC; PG; JST; Privacy; enhanced

Many countries, including the United States and Japan, are facing a rapidly aging population. Improving the quality of healthcare and quality of life for senior citizens while managing and even reducing the health and social care costs is a critical challenge. The growth and accessibility of wearable devices enable continuous monitoring of a person’s vital signs and other health indicators. These wearable data, combined with medical records and other community and environment data, are particularly valuable for senior communities in order to identify new conditions or relapses for early intervention. Collectively, such data from different individuals, communities, and countries, can be used to learn better predictive models and improve population health at large. This project aims to build a multidisciplinary team including academic researchers with complementary expertise (big data, privacy and security, machine learning, human-computer interaction, sociology, and mobile health) and community stakeholders (seniors, community service providers, healthcare providers, and government agencies), to understand the unique challenges and form a research agenda for developing and deploying a health monitoring system for senior communities. The project will study: 1) data integration and machine learning techniques to integrate data from multiple sources in real time for monitoring and intervention; and to leverage the data from different communities to improve healthcare outcome and medical research; 2) privacy-enhancing techniques including differential privacy and federated learning to ensure the system is compliant with regulations, while balancing the privacy protection and utility of the system; and 3) social implications and cultural differences of the technology in the two countries via online surveys and qualitative studies to identify challenges and barriers in health monitoring for senior communities, and their impact on the design and adoption of the proposed technology. The project includes a set of community engagement activities in order to develop a research agenda that can not only empower the senior communities and improve their health and well-being, but also enable data-driven medical research that improves population health at large.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.

包括美国和日本在内的许多国家都面临着人口迅速老龄化的问题，提高老年人的医疗保健质量和生活质量，同时管理甚至降低健康和社会护理成本是一项严峻的挑战。可穿戴设备能够持续监测一个人的生命体征和其他健康指标，这些可穿戴数据与医疗记录和其他社区和环境数据相结合，对于老年人社区来说特别有价值，可以帮助他们发现新的病情或旧病复发，从而进行早期干预。这些数据来自不同的个人、社区、该项目旨在建立一个多学科团队，包括具有互补专业知识（大数据、隐私和安全、机器学习、人机交互、社会学、移动医疗）和社区利益相关者（老年人、社区服务提供者、医疗保健提供者和政府机构），了解独特的挑战并制定为老年人社区开发和部署健康监测系统的研究议程。该项目将研究：1）数据集成和机器学习技术来集成数据实时监控和干预多个来源；并利用来自不同社区的数据来改善医疗保健结果和医学研究；2）隐私增强技术，包括差异隐私和联合学习，以确保系统符合法规，同时平衡隐私保护和系统的实用性；3) 通过在线调查和定性研究，确定该技术在两国的社会影响和文化差异，以确定老年人社区健康监测的挑战和障碍，及其对设计和采用的影响该项目包括一组社区技术。参与活动，以制定研究议程，该议程不仅可以增强老年人社区的权能并改善他们的健康和福祉，而且还可以实现数据驱动的医学研究，从而改善广大人口的健康。该奖项符合 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。

项目成果

REACT:Real-Time Contact Tracing and Risk Monitoring via PrivacyEnhanced Mobile Tracking

REACT：通过隐私增强型移动跟踪进行实时接触者追踪和风险监控

• 发表时间: 2021-01
• 期刊: IEEE International Conference on Data Engineering
• 作者: Da, Yanan;Ahuja, Ritesh;Xiong, Li;Shahabi, Cyrus
• 通讯作者: Shahabi, Cyrus

Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

邻居的回声：使用 Shuffle 模型实现个性化私人联邦学习的隐私放大

• 发表时间: 2023-04
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Liu, Yixuan;Zhao, Suyun;Xiong, Li;Liu, Yuhan;Chen, Hong
• 通讯作者: Chen, Hong

Private Stochastic Non-convex Optimization with Improved Utility Rates

提高利用率的私有随机非凸优化

• DOI: 10.24963/ijcai.2021/464
• 发表时间: 2021-08-01
• 期刊: Journal of Sound and Vibration
• 影响因子: 4.7
• 作者: Qiuchen Zhang;Jing Ma;Jian Lou;Li Xiong
• 通讯作者: Li Xiong

PrivLBS: Local Differential Privacy for Location-Based Services with Staircase Randomized Response

PrivLBS：具有阶梯随机响应的基于位置的服务的本地差分隐私

• 发表时间: 2022-01
• 期刊: Proceedings of the ACM Conference on Computer and Communications Security
• 作者: Wang, Han;Hong, Hanbin;Xiong, Li;Qin, Zhan;Hong, Yuan
• 通讯作者: Hong, Yuan

Communication Efficient Tensor Factorization for Decentralized Healthcare Networks

去中心化医疗网络的通信高效张量分解

• DOI: 10.1109/icdm51629.2021.00147
• 发表时间: 2021-12
• 期刊: 2021 IEEE International Conference on Data Mining (ICDM
• 作者: Ma, Jing;Zhang, Qiuchen;Lou, Jian;Xiong, Li;Bhavani, Sivasubramanium;Ho, Joyce C.
• 通讯作者: Ho, Joyce C.