SCH: INT: Collaborative Research: Crowd in Action: Human-Centric Privacy-Preserving Data Analytics for Environmental Public Health

虽然目前的医疗保健系统积极收集医院患者的医疗数据，但由于健康相关数据的高度敏感性，大量的个人主观数据在环境公共卫生分析中往往被忽视。因此，缺乏实时监测数据，如高危人群的症状报告和严重的环境污染，导致有效预防疫情传播的潜伏期长得出了名。该项目旨在解决在保护隐私的情况下从多个来源收集和分析环境公共卫生的多尺度数据方面的根本挑战。已开发的技术使社区中的每个人能够主动贡献自己和周围环境的实时数据，以改善公共健康，而不会损害他/她的隐私。此外，该项目还为未来的决策者和工作人员提供隐私保护医疗技术方面的培训。这项多学科的研究通过结合多尺度数据收集和分析来促进最先进的公共卫生。具体地说，该项目重新设计了针对严重传染病和长期与环境相关的疾病及其恶化(例如，空气污染物引起的肺部疾病，如慢性阻塞性肺病和肺癌)的现有医疗监测系统。考虑到患者和用户数据的高度敏感性和分布式方式，该项目从两个方面解决隐私保护问题：1)在不泄露个人隐私的情况下，应用新的度量标准，彻底重新设计高效的协作分类方案；2)引入新的架构，使用轻量级和可验证的加密方案执行众包数据分析。该项目还将理论结果应用于实际的人群感知系统和社交网络进行验证。最后，提出了一种新的公共健康预测模型方法，并在医疗卫生系统中进行了系统的实际应用。

Although current healthcare systems actively collect medical data from patients in hospitals, numerous personal subjective data is commonly neglected in the analysis of environmental public health due to high-sensitivity of health-related data. As a result, there is a lack of real-time monitoring data, such as symptom reports from high-risk groups and severe environmental pollution, causing notoriously long latency for effective prevention of the spread of epidemic diseases. This project is to address the fundamental challenges on collecting and analyzing multi-scale data from multi-sources for environmental public health in a privacy-preserving manner. The developed technologies empower each individual in a community to proactively contribute real-time data of themselves and surroundings for the betterment of public health without compromising his/her privacy. In addition, this project also serves as a training ground for educating future decision-makers and workforce on privacy-preserving healthcare technologies.This multidisciplinary research advances the state-of-the-art public health by combining multi-scale data collection and analysis. Specifically, the project redesigns current healthcare monitoring systems for both severe infectious diseases and long-term environment-related diseases and their exacerbation (e.g., air pollutant-induced pulmonary diseases, such as chronic obstructive pulmonary disease and lung cancer). By considering the high sensitivity and distributed manner of the data from patients and users, this project addresses the privacy preservation in two-fold: 1) completely redesign efficient collaborative classification schemes by applying novel metrics without leaking individual's privacy; and 2) introduce new architectures to perform crowdsourcing data analysis by using light-weighted and verifiable encryption schemes. This project also grounds the theoretical outcomes to actual crowdsensing systems and social networks for validation. Finally, a new methodology on public health prediction model is developed with practical systematic implementation in healthcare systems.