EAGER: Towards a Computational Infrastructure for Analysis of Sensitive Data

EAGER：建立用于分析敏感数据的计算基础设施

• 批准号: 1551843
• 负责人: Vasant Honavar
• 金额: $ 23.16万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551843&HistoricalAwards=false
• 关键词: EAGER; Towards; Computational; Infrastructure; Analysis

In many applications, e.g., health, education, our ability to realize the full potential of big data to improve decisions and outcomes is currently limited, by the lack of practical frameworks for analysis of sensitive data in a manner that does not violate applicable data access and use policies. This constitutes a significant barrier to the engagement of researchers with expertise in analytics in developing and evaluating advanced methods for analysis of such data, assessing the performance of alternative approaches, or ensuring the reproducibility of results. Against this background, this high-risk and potentially high-impact research project aims to explore a framework and a software infrastructure for data access and use policy (DAUP) compliant analysis of sensitive data. The project aims to develop a novel framework for data access and use policy (DAUP) compliant analysis of sensitive data. The framework will support (i) Querying and retrieval of information from the data store that are permitted by the user or project specific DAUP. Such information could include the schema of the data store, metadata that specify the variables, and their domains and ranges, etc.; (ii) Execution of system or user-supplied implementations of algorithms for construct predictive or causal models or visualizations from the data in the data store; (iii) Evaluation of the predictive performance of the resulting models on benchmark data or user-provided data; (iv) Deployment of the validated models in the form of web servers that provide predictions or visualizations over user-submitted data or over results of user-defined queries against the data store; and (v) Publication of reusable analytics workflows. This exploratory project seeks to test the feasibility of the framework using predictive and causal modeling of data from an online health community as a test case. A major outcome of this project is the open source software infrastructure for facilitating analysis and visualization of sensitive data. This research will: (i) fill a major gap in infrastructure for predictive modeling from sensitive data; (ii) significantly lower the barrier to the entry of researchers with deep expertise in analytics to domains (e.g., health, education) that involve sensitive data; (iii) improve the accuracy of assessment of the state-of-the-art in predictive and causal modeling in such domains by facilitating rigorous comparison of algorithms; and (iv) facilitate, reproducible analysis of sensitive data. This research will (i) yield a prototype open source software infrastructure to support analysis and visualization of sensitive data; (ii) Accelerate data-driven advances in domains that involve sensitive data e.g., health, education through broad engagement of talent in developing better algorithms; and (iii) Support incorporation of hands-on experience with such applications into Data Sciences education through hackathons and competitions organized around specific sensitive data sets.

在许多应用程序，例如健康，教育中，我们实现大数据的全部潜力改善决策和结果的能力目前受到限制，因为缺乏实用的框架来分析敏感数据，以不违反适用的数据访问的方式来分析敏感数据并使用政策。这构成了具有分析专业知识的研究人员在开发和评估分析此类数据，评估替代方法的性能或确保结果可重复性的高级方法方面的专业知识的重要障碍。在这种背景下，这个高风险且潜在的高影响力研究项目旨在探索用于数据访问和使用策略（DAUP）符合敏感数据的框架和软件基础架构。 该项目旨在为数据访问和使用策略（DAUP）对敏感数据的兼容分析开发一个新颖的框架。该框架将支持（i）从数据存储中查询和检索用户或特定于项目的DAUP允许的信息。这些信息可以包括数据存储的模式，指定变量的元数据及其域和范围等。 （ii）从数据存储中的数据中执行系统或用户提供的算法实现算法或因果模型或可视化的算法； （iii）评估基准数据或用户提供的数据上所得模型的预测性能； （iv）以Web服务器的形式部署经过验证的模型，该模型对用户提取的数据或针对数据存储的用户定义查询的结果提供了预测或可视化； （v）出版可重复使用的分析工作流程。该探索性项目旨在使用从在线健康社区中的数据进行预测和因果建模来测试框架的可行性。 该项目的主要结果是用于促进敏感数据分析和可视化的开源软件基础架构。这项研究将：（i）从敏感数据中填补用于预测建模的基础架构的主要空白； （ii）显着降低了具有深厚分析专业知识的研究人员进入涉及敏感数据的领域（例如健康，教育）的障碍； （iii）通过促进算法的严格比较，提高了此类领域中最先进和因果建模的评估的准确性； （iv）促进敏感数据的可再现分析。这项研究将（i）产生原型开源软件基础架构，以支持敏感数据的分析和可视化； （ii）在涉及敏感数据的领域中加速了数据驱动的进步，例如健康，通过在开发更好的算法方面的广泛参与人才通过教育； （iii）通过围绕特定敏感的数据集组织的黑客马拉松和竞赛，支持将动手经验与此类应用程序与此类应用程序一起纳入数据科学教育。