TC: Small: Analysis for a Cloud of Policies: Foundations and Tools

TC：小型：政策云分析：基础和工具

• 批准号: 1116557
• 负责人: Daniel Dougherty
• 金额: $ 49.96万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116557&HistoricalAwards=false
• 关键词: TC; Small; Analysis; Cloud; Policies

Computers and people live in a world governed by policy. At the lowest level, policies determine how information flows within networks; at the highest level, they describe how users' personal information is shared across applications. Of course, end-users, as policy authors, make mistakes: rules can have unintended consequences and multiple policies can interact in ways that their authors didn't intend. Users can benefit from tools to help them understand the policies they write and maintain. Policy analysis refers to rigorous methods for detecting these situations before they cause harm. Given the personal and sensitive information migrating to cloud-based applications, analyzers for mainstream policy contexts have clear impact on computing infrastructure.Prior research has shown that first-order logic is a suitable foundation for capturing and analyzing many application- and network-based policies. In particular, analyses based on scenario-finding provide concrete and detailed feedback for policy authors. This project focuses on techniques to make scenario-finding effective for real-world policies based on first-order logic. Specifically, the project pursues (1) foundational research on algorithms for model-finding in first-order logic, (2) methods to analyze federations of policies across multiple levels of abstraction (such as found in cloud computing), (3) handling additional policy features such as numeric constraints and dynamic network routing, and (4) methods to automatically generate repairs to policies against requirements or scenarios. The PIs' existing Margrave policy analyzer serves as a testbed and deployment vehicle for this research.

计算机和人们生活在一个受政策管辖的世界中。 在最底层，策略决定信息如何在网络内流动；在最高级别，它们描述了如何在应用程序之间共享用户的个人信息。 当然，作为策略制定者的最终用户会犯错误：规则可能会产生意想不到的后果，并且多个策略可能会以作者没有想到的方式进行交互。 用户可以受益于帮助他们理解他们编写和维护的策略的工具。 政策分析是指在这些情况造成损害之前检测到这些情况的严格方法。 鉴于个人和敏感信息迁移到基于云的应用程序，主流策略上下文的分析器对计算基础设施产生了明显的影响。先前的研究表明，一阶逻辑是捕获和分析许多基于应用程序和网络的策略的合适基础。 特别是，基于情景发现的分析为政策制定者提供了具体而详细的反馈。 该项目重点关注使情景查找对基于一阶逻辑的现实世界政策有效的技术。具体来说，该项目致力于 (1) 一阶逻辑中模型查找算法的基础研究，(2) 分析跨多个抽象级别的策略联合的方法（例如云计算中的策略联合），(3) 处理额外的策略功能，例如数值约束和动态网络路由，以及 (4) 根据需求或场景自动生成策略修复的方法。 PI 现有的 Margrave 策略分析器充当本研究的测试平台和部署工具。