EAGER: Secure Workflow Provenance for Collaborative Data Analytics

EAGER：协作数据分析的安全工作流程来源

• 批准号: 1747095
• 负责人: Jia Zhang
• 金额: $ 20万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747095&HistoricalAwards=false
• 关键词: EAGER; Secure; Workflow; Provenance; Collaborative

Collaborative data analysis has become a necessity and trend in the era of big data. In such collaborative environments, intellectual property protection mechanisms are critical to maintain and encourage research partnerships. Such mechanisms shall protect not only data sources and data analysis algorithms, but also protect data provenance, i.e., data processing history. For example, participating parties may request to secure their access and sharing of various kinds of data products (source, intermediate, and final), processing steps, and their inter-dependencies. However, existing mechanisms do not provide such fine-grained protection on multi-step data analytics procedure (workflow) provenance. To address such a challenge, this project aims to study and explore novel mechanisms to secure the access and querying over collaborative scientific workflow provenance.The technical goal of this project is to understand in depth about  the feasibility of dataflow provenance-oriented access and querying mechanisms. This high-risk, high-reward work will produce the following two outcomes: (1) a multi-level fine-grained secure provenance access and querying mechanism for provenance collection, including sensitive data as well as sensitive dependencies between data, tasks, and users, and (2) automated analysis algorithms to ensure that provenance access and querying policies would conform to desirable constraints on evolving dependencies. The intended techniques and tool will be evaluated in genomic data analysis domain to demonstrate its usability and significance in the context of collaborative data analytics. The expected techniques will be equipped to the NSF-sponsored collaborative scientific workflow tool for secure data analytics collaboration.

协同数据分析已经成为大数据时代的必然和趋势。在这种合作环境中，知识产权保护机制对于维持和鼓励研究伙伴关系至关重要。这种机制不仅应保护数据源和数据分析算法，而且还应保护数据来源，即，数据处理历史。例如，参与方可能会请求保护其对各种数据产品（源、中间和最终）、处理步骤及其相互依赖性的访问和共享。然而，现有的机制不提供这种细粒度的保护多步骤数据分析过程（工作流）的出处。为了解决这一问题，本项目旨在研究和探索新的机制，以确保协作科学工作流溯源访问和查询的安全性，深入理解面向协作科学工作流溯源访问和查询机制的可行性。这种高风险、高回报的工作将产生以下两个结果：（1）一个多级细粒度的安全起源访问和查询机制，用于起源收集，包括敏感数据以及数据、任务和用户之间的敏感依赖关系，以及（2）自动分析算法，以确保起源访问和查询策略符合不断发展的依赖关系的期望约束。预期的技术和工具将在基因组数据分析领域进行评估，以证明其在协作数据分析背景下的可用性和重要性。预期的技术将配备到NSF赞助的协作科学工作流程工具中，以实现安全的数据分析协作。