Collaborative Research: Secure Provenance in High-End Computing Systems

协作研究：高端计算系统的安全来源

• 批准号: 0937833
• 负责人: Radu Sion
• 金额: $ 56.5万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0937833&HistoricalAwards=false
• 关键词: Collaborative; Research; Secure; Provenance; End

Data provenance documents the inputs, entities, systems, and processes that influence data of interest---in effect providing a historical record of the data and its origins. The generated evidence supports essential forensic activities such as data-dependency analysis, error/ compromise detection and recovery, and auditing and compliance analysis. This collaborative project is focused on theory and systems supporting practical end-to-end provenance in high-end computing systems. Here, systems are investigated where provenance authorities accept host- level provenance data from validated provenance monitors, to assemble a trustworthy provenance record. Provenance monitors externally observe systems or applications and securely record the evolution of data they manipulate. The provenance record is shared across the distributed environment. In support of this vision, tools and systems are explored that identify policy (what provenance data to record), trusted authorities (which entities may assert provenance information), and infrastructure (where to record provenance data). Moreover, the provenance has the potential to hurt system performance: collecting too much provenance information or doing so in an inefficient or invasive way can introduce unacceptable overheads. In response, the project is further focused on ways to understand and reduce the costs of provenance collection.

数据来源记录了影响感兴趣数据的输入、实体、系统和流程——实际上提供了数据及其来源的历史记录。生成的证据支持基本的取证活动，例如数据依赖性分析、错误/妥协检测和恢复以及审计和合规性分析。该合作项目专注于支持高端计算系统中实际端到端起源的理论和系统。在这里，对系统进行调查，来源当局接受来自经过验证的来源监视器的主机级来源数据，以收集值得信赖的来源记录。 Provenance 监控外部观察系统或应用程序，并安全地记录他们操作的数据的演变。出处记录在分布式环境中共享。为了支持这一愿景，我们探索了工具和系统来识别政策（记录哪些来源数据）、受信任的权威机构（哪些实体可以断言来源信息）和基础设施（在哪里记录来源数据）。此外，来源有可能损害系统性能：收集太多来源信息或以低效或侵入性方式收集可能会带来不可接受的开销。作为回应，该项目进一步关注了解和降低来源收集成本的方法。