Decentralized differentially-private methods for dynamic data release and analysis

用于动态数据发布和分析的去中心化差分隐私方法

• 批准号: 9239100
• 负责人: Xiaoqian Jiang
• 金额: $ 61.12万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9239100
• 关键词: Characteristics; Clinical Data; Collaborations; Computer software; Data; Data Analyses; Data Element; Data Science; Data Set; Data Sources; Databases; Decentralization; Docking; Ensure; Environment; Family member; Goals; Healthcare; Individual; Institution; Intuition; Knowledge; Link; Logistic Regressions; Methods; Modeling; Noise; Output; Patients; Performance; Play; Privacy; Privatization; Process; Public Domains; Publications; Quality of Care; Research; Risk; Role; Sample Size; Secure; Security; Statistical Data Interpretation; System; Trust; Update; Work; authority; base; cryptography; data sharing; design; distributed data; genomic data; improved; individual patient; novel; open source; patient subsets; repository; software development; statistics; web interface

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.

项目摘要 数据共享和信息交换在生物医学数据中发挥着关键作用 科学提高护理质量，加速发现，并促进有意义的 二次使用临床数据。但隐私是公众关心的一个大问题。现有 分布式数据分析方法不能解决 交换中间统计信息，并且它们不能处理动态数据库更新 很好.本项目旨在设计和实施差异化私有 动态数据传播和分析的分散方法。我们计划使用 来自公共领域和当地机构的基因组和临床数据（UCSD和 Emory）仔细评估我们提出的新方法的可行性和效率。