Decentralized differentially-private methods for dynamic data release and analysis

项目总结 数据共享和信息交换在生物医学数据中发挥着至关重要的作用 科学提高护理质量，加速发现，促进有意义的 二次利用临床资料。但隐私是公众的一大担忧。现有 分布式数据分析方法不能解决安全和隐私问题 交换中间统计数据，并且它们无法处理动态数据库更新 很好。该项目旨在设计和实施差异化-私有 用于动态数据传播和分析的分散方法。我们计划使用 来自公共领域和地方机构的基因组和临床数据(UCSD和 埃默里)仔细评估我们提出的新方法的可行性和效率。

PROJECT SUMMARY Data sharing and information exchange are playing critical roles in biomedical data science to improve quality of care, accelerate discovery, and promote meaningful secondary use of clinical data. But privacy is a big concern to the public. Existing distributed data analysis methods do not address the security and privacy issues in exchanging intermediary statistics and they cannot handle dynamic database updates very well. This project aims at designing and implementing differentially-private decentralized methods for dynamic data dissemination and analysis. We plan to use genomic and clinical data from both public domain and local institutions (UCSD and Emory) to carefully evaluate the feasibility and efficiency of our proposed new methods.