保护隐私和转换COVID-19病例监测数据集供公众使用
Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use
摘要：
目的：促进联邦收集数据共享的联邦开放数据计划对于透明度，数据质量，信任以及与公众和州，部落，地方和领土（STLT）合作伙伴的关系非常重要。这些举措通过向更多的研究人员、科学家和政策制定者提供数据，以供分析、协作和在疾病预防控制中心应急人员之外的有价值的使用，来促进对健康状况和疾病的了解。对于新出现的情况（如COVID-19），尤其如此，我们有很多东西要学习，并有不断变化的数据需求。自疫情爆发以来，CDC已经从司法管辖区收集了个人级别的去识别数据，目前有超过800万条记录，每天都在增加。本文介绍了CDC如何从这些收集的数据中设计和生成两个去识别的公共数据集。材料与方法：根据有用性、公众要求和隐私影响纳入数据元素;抑制特定字段值以降低重新识别和暴露机密信息的风险。使用数据管理平台分析工具以及R脚本创建并验证数据集的隐私性和机密性。结果：不受限制的数据可通过Data.CDC.gov向公众提供，而受限制的数据（带有额外字段）可通过GitHub.com上的私有存储库通过数据使用协议提供。实践启示：对可用的公共数据、用于创建这些数据的方法以及用于保护去身份化个人隐私的算法的丰富理解允许改进数据使用。自动化数据生成程序允许更大和更及时地共享数据。
英文摘要：
Objectives: Federal open data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial (STLT) partners. These initiatives advance understanding of health conditions and diseases by providing data to more researchers, scientists, and policymakers for analysis, collaboration, and valuable use outside CDC responders. This is particularly true for emerging conditions such as COVID-19 where we have much to learn and have evolving data needs. Since the beginning of the outbreak, CDC has collected person-level, de-identified data from jurisdictions and currently has over 8 million records, increasing each day. This paper describes how CDC designed and produces two de-identified public datasets from these collected data. Materials and Methods: Data elements were included based on the usefulness, public request, and privacy implications; specific field values were suppressed to reduce risk of reidentification and exposure of confidential information. Datasets were created and verified for privacy and confidentiality using data management platform analytic tools as well as R scripts. Results: Unrestricted data are available to the public through Data.CDC.gov and restricted data, with additional fields, are available with a data use agreement through a private repository on GitHub.com. Practice Implications: Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect privacy of de-identified individuals allow for improved data use. Automating data generation procedures allows greater and more timely sharing of data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
