Data Sharing and Privacy Protection in Digital-Age Environmental Health Studies

描述（由申请人提供）：用于化学生物监测和个人特定采样的新环境暴露技术，例如在家中或呼吸空间中，是发现疾病的环境原因和指导预防的关键进展。这些方法为NIH的主要队列研究和监测提供了有价值的暴露数据，特别是NHANES对美国人口中200多种化学品的生物监测和新的EPA ExpoCast。个人暴露测量是昂贵的，有时是不可重复的，这激励了开放数据共享，包括在线存储库。与此同时，个人测量引发了新的伦理担忧，即研究参与者的身份可能会被泄露，即使是在被认为是匿名的数据中，这一过程被称为重新识别。激增的公共数据和计算能力将继续增加隐私风险。在可见的重新识别实例的推动下，医疗保健和遗传学研究人员正在讨论和研究编辑共享数据的新做法，警告参与者隐私风险，有时要求“公开同意”共享数据而不保护隐私。然而，尚未对环境化学品暴露数据的计算隐私风险进行调查。这些数据可能会通过使用诸如房地产、环境合规、许可、天气和消费者购买等数据的新颖链接策略带来风险。重新识别可能会给“受污染”的个人或社区带来耻辱，暴露个人认为隐私的行为（例如，吸烟或使用美国禁止的海外产品），在测量受管制化学品时触发法律义务，或影响财产价值、保险或就业能力。本项目将对隐私进行实证评估

DESCRIPTION (provided by applicant): New environmental exposure technologies for chemical biomonitoring and person-specific sampling, such as in homes or breathing space, are crucial advances for finding environmental causes of disease and guiding prevention. These methods yield valuable exposure data for major NIH cohort studies and for surveillance, notably NHANES biomonitoring for more than 200 chemicals in the US population and the new EPA ExpoCast. Personal exposure measurements are expensive and sometimes non-repeatable, motivating open data- sharing, including in online repositories. At the same time, personal measurements raise new ethical concerns about the possibility that the identity of study participants might be revealed even in data considered anonymized, a process called re-identification. Proliferating public data and computing power will continue to increase privacy risks. Prompted by visible instances of re-identification, healthcare and genetics researchers, among others, are debating and investigating new practices for redacting shared data, warning participants of privacy risks, and sometimes requesting "open consent" to share data without protecting privacy. However, computational privacy risks have not yet been investigated for environmental chemical exposure data. These data may pose risks through novel linkage strategies using data such as on real estate, environmental compliance, permits, weather, and consumer purchases. Re-identification could result in stigma for "contaminated" individuals or communities, reveal behavior a person considers private (e.g., smoking or use of overseas products banned in the US), trigger legal obligations if a regulated chemical is measured, or affect property values, insurance, or employability. This project will empirically evaluate privacy risks and develop solutions for environmental health studies. It will engage an Advisory Council of environmental health scientists, computer scientists, policymakers, community leaders, and bioethicists to provide input and seek consensus on complex ethical and values-based considerations. Building on the investigators' established computational model for health data, this study will develop a model for predicting re-identification risk in environmental health studies. By applying the model to 10 important environmental studies, the project will quantify privacy risks in this field and identify specific data fields that contribute to risk. The model wil be validated by testing the actual number of re-identifications in a household exposure study. Based on results indicating risky data fields, the study will test and seek to optimize procedures to redact or mask data to improve privacy while retaining scientific utility for data-sharing. Because data-sharing decisions ultimately rest on participants' informed consent, the project complements computational analyses by asking participants in two large, innovative online studies about their understandings and values related to privacy and data-sharing. Results from this project will provide researchers with ethically and technically sound methods for sharing environmental data, contributing to more-rapid discovery of preventable causes of disease.