Protection of Records: Privacy (PReP) Technology for Medical Research

描述（由申请人提供）：鉴于可能侵犯个人隐私，在传播临床数据时披露敏感信息越来越受到关注。数据共享已成为加速生物医学研究和提高医疗保健质量的关键。我们将开发新的隐私保护方法，以适应传播的数据量和某些变量的敏感性。我们的第一个目标是测量患者亚群中个人记录的细粒度隐私风险，该指标可用于监测和定制个人临床记录的隐私保护，并帮助确定隐私保护的优先级。第二个目标是开发一种新的实用方法，以支持在集中式和分布式环境中保护隐私的数据传播，无论是否知道哪种分析技术将应用于公开的数据。第三个目标是通过先进的并行化技术来加速隐私保护算法。如果成功，这些新方法将为实时的大数据集传播/分析提供隐私保护。这些目标忠实于美国国家医学图书馆的使命，它们与导师们作为国家生物医学计算中心（NCBC） iDASH（整合数据进行分析、匿名化和共享）的一部分，领导可信数据共享和个性化预测模型发展的努力密切相关。

DESCRIPTION (provided by applicant): There is increasing concern in disclosing sensitive information when clinical data are disseminated, given the potential for breach of individual privacy. Data sharing has become critical in the acceleration of biomedical research and healthcare quality improvement. We will develop new methods for privacy protection that can adapt to the amount of the data being disseminated and the sensitivity of certain variables. Our first aim is to measure fine-grained privacy risk of individual records in patient sub- populations This index can be used to monitor and customize privacy protection of individual clinical records and help prioritize efforts in privacy protection. The second aim is to develop a new and practical method to support privacy-preserving data dissemination in both centralized and distributed environments, with or without knowledge of which analytic techniques will be applied to the disclosed data. The third aim is to speed up privacy preserving algorithms through advanced parallelization techniques. If successful, these new methods will allow privacy protection for large data set dissemination/analysis in real time. These aims are faithful to the mission of the National Library of Medicine, and they are tightly related to the mentors' efforts i leading the development of trustworthy data sharing and individualized predictive models as part of the National Center for Biomedical Computing (NCBC), iDASH (integrating Data for analysis, Anonymization, and SHaring). The applicant wishes to use this funding opportunity to complement his computer science skills with biomedical knowledge, and specialized training in parallel computing to investigate new algorithms for privacy protection in disseminated data. Success in this project will lead to his long-term goal of becoming an independently funded investigator and joining the core faculty of the Division of Biomedical Informatics at UCSD.