TWC: Medium: Collaborative: Broker Leads for Privacy-Preserving Discovery in Health Information Exchange

对分布式数据集研究的支持受到涉众需求限制共享的挑战。研究人员需要早期访问，以确定数据集是否可能包含他们需要的数据。Broker Leads项目正在开发隐私增强技术，以适应数据驱动研究的发现阶段。它的方法受到基于代理系统的健康信息交换的启发，其中数据由医疗保健提供者持有，并在由代理管理的分布式查询中收集。这种系统具有支持公共卫生和生物医学研究的潜力。该项目针对“类似患者查询”，其中查询是患者的医疗记录，响应是关于类似患者的信息。这样的查询对许多应用程序都有价值，包括为寻找机构进一步讨论联合研究而建立队列。Broker Leads使用“lead”的概念，其中数据持有者提供具有代表性的不可识别的真实或合成数据集合，满足强大的隐私保证，例如差异隐私。尽管由于为隐私保护而进行的转换，这些数据可能不适合临床决策和科学发现，但它们可以指导broker lead系统的用户找到非常可能对解决给定的类似患者查询有用的数据集。然后，这些数据集可以与其他隐私保护策略一起使用，例如安全多方计算或限制性数据使用协议，以确保充分的数据保护。除了为医疗数据研究的早期阶段提供实用且经过充分分析的策略外，该项目还将为端到端应用程序中隐私技术的实际问题提供新的见解。

Support for research on distributed data sets is challenged by stakeholder requirements limiting sharing. Researchers need early stage access to determine whether data sets are likely to contain the data they need. The Broker Leads project is developing privacy-enhancing technologies adapted to this discovery phase of data-driven research. Its approach is inspired by health information exchanges that are based on a broker system where data are held by healthcare providers and collected in distributed queries managed by the broker. Such systems have potential to support public health and biomedical research. The project targets "similar patient queries" where the query is a patient medical record and the response is information about similar patients. Such queries have value for many applications, including developing cohorts for finding institutions for further discussions about joint research.Broker Leads uses the concept of a "lead" in which data holders provide representative collections of non-identifiable real or synthetic data meeting strong privacy guarantees, e.g., differential privacy. Even though such data may be unsuitable for clinical decision making and scientific discovery due to the transformations done for privacy protection, they guide a user of a broker lead system to the data sets very likely to be useful to addressing a given similar patient query. These data sets can then be used with other privacy-protecting strategies, such as secure multiparty computation or restrictive data use agreements ensuring adequate data protection. In addition to providing practical and well-analyzed strategies for early stages of research on healthcare data, this project will provide new insights into practical issues with privacy technology in end-to-end applications.