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

虽然目前的医疗系统积极收集患者在医院的医疗数据，许多个人的主观数据通常被忽视的环境公共卫生分析，由于高敏感性的健康相关的数据。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，导致有效预防流行病传播的延迟时间长。该项目旨在以隐私保护的方式解决收集和分析环境公共卫生多源多尺度数据的基本挑战。开发的技术使社区中的每个人都能够主动贡献自己和周围环境的实时数据，以改善公共卫生，而不会损害他/她的隐私。此外，该项目还为未来的决策者和劳动力提供了隐私保护医疗技术方面的培训基地。该多学科研究通过结合多尺度数据收集和分析，推动了最先进的公共卫生。具体而言，该项目重新设计了目前针对严重传染病和长期环境相关疾病及其恶化的医疗保健监测系统（例如，空气污染引起的肺部疾病，如慢性阻塞性肺病和肺癌）。通过考虑来自患者和用户的数据的高敏感性和分布式方式，该项目从两个方面解决了隐私保护问题：1）通过应用新的度量完全重新设计有效的协作分类方案，而不会泄露个人隐私; 2）引入新的架构，通过使用轻量级和可验证的加密方案来执行众包数据分析。该项目还将理论成果应用于实际的人群感知系统和社交网络进行验证。最后，一个新的方法，公共健康预测模型的开发与实际系统的实施在医疗系统。

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.