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

描述（由申请人提供）：考虑到可能侵犯个人隐私，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质量的关键。我们将开发新的隐私保护方法，以适应所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体记录的细粒度隐私风险。该指数可用于监控和定制个体临床记录的隐私保护，并有助于优先考虑隐私保护工作。第二个目标是开发一种新的实用方法来支持集中式和分布式环境中的隐私保护数据传播，无论是否知道哪些分析技术将应用于所披露的数据。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，这些新方法将允许真实的时间的大数据集传播/分析的隐私保护。这些目标忠实于国家医学图书馆的使命，并且与导师们的努力密切相关，他们领导了值得信赖的数据共享和个性化预测模型的开发，作为国家生物医学计算中心（NCBC）的一部分，iDASH（整合数据用于分析，分析和共享）。 申请人希望利用这一资助机会，以补充他的计算机科学技能与生物医学知识，并在并行计算的专门培训，以研究新的算法，在传播的数据隐私保护。在这个项目的成功将导致他的长期目标，成为一个独立资助的研究人员，并加入生物医学信息学在UCSD的部门的核心教师。

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.