RAPID: Collaborative: A Privacy Risk Assessment Framework for Person-Level Data Sharing During Pandemics

2019冠状病毒病大流行表明，共享数据对于建立更好的统计流行病学模型、促进（公共和私营部门）决策以及确保公众健康至关重要。此外，形势发展迅速，这表明需要不断及时地进行数据共享。迄今为止，已经进行的大部分数据共享都集中在汇总统计（例如，事件计数）上，然而，一些最重要的数据是在个人层面上的，这对于直观了解合并症如何影响健康结果以及从时空角度对疾病轨迹进行建模至关重要。这些数据被大量希望支持这些努力的服务提供商获取，但担心这样做会侵犯相应个人的隐私权，特别是他们的匿名性。为了能够及时、有用和保护隐私地发布患者特定的COVID-19数据，该项目旨在开发和传播新的隐私风险评估技术，并在工作软件中实施，以帮助数据管理人员和公共卫生官员对隐私风险（根据现行法律，重点是重新识别）和公共数据效用之间的权衡进行思考。该项目将提供共享COVID-19确诊或疑似患者特定数据所需的最佳做法和工具。该项目将通过考虑不断变化的隐私风险和数据效用，开发新的动态隐私风险评估模型，用于披露数据，以支持流行病学调查（特别是大流行病）。在此过程中，将对拟议的模型进行定制，以便通过建模更丰富的数据属性空间，特别是对于建模与生物制剂（如COVID-19）相关的流行病学风险因素非常重要的数据属性空间，来披露地理、人口统计学和临床相关的现象（例如，基于药物处方或购买的健康适应症）。为了对不断演变的隐私风险进行建模，将开发考虑多种类型的潜在重新识别攻击和用于发布相同数据的多个版本的数据编校的隐私风险估计模型。此外，拟议的模型将面向支持特定于生物监测工作的实用功能，包括那些为COVID-19建模和应对而出现的功能。最后，为确保拟议的方法可广泛获取和重用，将发布一种开源软件工具，使数据保管人，特别是公共卫生当局能够在共享数据时做出明智的决定，适当平衡公共卫生目标和个人隐私。该奖项反映了美国国家科学基金会的法定使命，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 COVID-19 pandemic has demonstrated that sharing data is critical to building better statistical epidemiological models, enabling policy decisions (in the public and private sector), and assuring the health of the public. Moreover, the situation has evolved quickly, indicating that data sharing needs to take place repeatedly and in a timely manner. To date, much of the data sharing that has taken place has focused on aggregate statistics (e.g., counts of events), yet some of the most important data is at the person-level, which is critical to providing intuition into how comorbidities influence health outcomes and model the trajectory of the disease in a temporal-spatial perspective. This data is captured by a large number of service providers who wish to support these endeavors, but are concerned that doing so will infringe upon the privacy rights of the corresponding individuals, particularly their anonymity. To enable timely, useful and privacy-preserving releases of patient specific COVID-19 data, this project aims to develop and disseminate novel privacy-risk assessment techniques, implemented in working software, to assist data managers, as well as public health officials, to reason about the tradeoffs between privacy risks (with a focus on re-identification, according to current law) and public data utility. The project will provide the best practices and tools needed for sharing patient-specific data about individuals diagnosed with, or suspected of, COVID-19. This project will develop novel, and dynamic privacy risk assessment models for disclosing data in support of epidemiological investigations (and particularly pandemics) by considering evolving privacy risks and data utility. In doing so, the proposed models will be tailored to enable the disclosure of geographic-, demographic-, and clinically-relevant phenomena (e.g., health indications based on pharmaceutical prescriptions or purchases) by modeling a much richer data attribute space, specifically one that is important for modeling epidemiologic risk factors associated with biological agents, such as COVID-19. To model evolving privacy risks, privacy risk estimation models that consider multiple types of potential re-identification attacks and data redactions used to release multiple versions of the same data will be developed. Furthermore, the proposed models will be oriented to support utility functions that are specific to bio-surveillance efforts, including those which have emerged for COVID-19 modeling and response. Finally, to ensure that the proposed approach is accessible and reusable widely, an open source software tool, that enables data custodians, and particularly public health authorities, to make informed decisions appropriately balancing public health goals with personal privacy when sharing data, will be released.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.