CICI:UCSS:Improving the Privacy and Security of Data for Wastewater-based Epidemiology

基于水的流行病学（WBE）领域收集和分析与公共卫生有关的污水系统的数据。这些数据包括身份、种族、行为、消费、污染和病原体感染的遗传和化学生物标志物。对WBE的兴趣最近爆发了，因为研究人员转向废水样本来估计当地人群中的SARS-CoV-2感染水平，并为公共卫生应对大流行提供信息。由于从废水中收集的数据是在人口水平上汇总的，因此通常假定这些数据是匿名的，因此不受健康隐私或其他监管保护的约束。这些数据在共享时最有用，但对于个人以及社区，学校和政府来说，共享会带来安全和隐私问题。 该项目正在开发技术和工具，以支持WBE数据集的合法使用，同时最大限度地减少滥用，并正在设计保护个人和组织隐私的协议。 这些工具将鼓励广泛参与网络经济联盟，使各组织能够保护其数据不被不利使用，这反过来又将有助于改善公众的健康，降低发病率和死亡率，该项目处理三个主要的安全威胁：个人查询造成的威胁;通过联合查询或大规模关联研究共享造成的威胁;以及废水样本中存在人类遗传物质所造成的问题。 这些威胁通过以下方式减轻：（1）使用同态加密支持对废物数据的安全查询;（2）基于安全计算实现联邦数据分析;以及（3）使用差异隐私保护基因组隐私。这些组件正在实施，以允许不同的实体在联合数据库上执行查询处理和训练机器学习模型，同时维护每个数据库的隐私和格式。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。

The field of Wastewater-Based Epidemiology (WBE) collects and analyzes data from sewage systems that relate to public health. These data include genetic and chemical biomarkers of identity, ethnicity, behavior, consumption, pollution, and pathogenic infections. Interest in WBE exploded recently, as researchers turned to wastewater samples to estimate SARS-CoV-2 infection levels in local populations and inform public health responses to the pandemic. Because data collected from wastewater are aggregated at the population level, they are typically assumed to be anonymous, and are therefore not subject to health privacy or other regulatory protections. These data are most useful when shared, yet sharing raises security and privacy issues, for individuals as well as neighborhoods, schools and governments. The project is developing technology and tools to support legitimate uses of WBE datasets while minimizing abuse, and it is designing protocols for protecting individual and organizational privacy. These tools will encourage wide participation in WBE consortia by enabling organizations to protect their data from adverse uses, which in turn will help improve the health of the general public and reduce morbidity and mortality.The project addresses three key security threats: those posed by individual queries; those posed by sharing, whether through joint queries or large-scale association studies; and those posed by the presence of human genetic material in wastewater samples. These threats are mitigated by: (1) supporting secure queries on wastewaster data using homomorphic encryption; (2) enabling federated data analysis based on secure computation; and (3) protecting genomic privacy with differential privacy. These components are being implemented to allow different entities to execute query processing and train Machine Learning models on joint databases while maintaining the privacy and format of each database. The system is being evaluated using real-world datasets with use cases that match important threat scenarios.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.