TC: Large: Collaborative Research: Privacy-Enhanced Secure Data Provenance

TC：大型：协作研究：隐私增强的安全数据来源

• 批准号: 1111512
• 负责人: Elisa Bertino
• 金额: $ 90万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing 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=1111512&HistoricalAwards=false
• 关键词: TC; Large; Collaborative; Research; Privacy

Data provenance refers to the history of the contents of an object and its successive transformations. Knowledge of data provenance is beneficial to many ends, such as enhancing data trustworthiness, facilitating accountability, verifying compliance, aiding forensics, and enabling more effective access and usage controls. Provenance data minimally needs integrity assurance to realize these benefits. Additionally, provenance data may need assurances of confidentiality (e.g., protect the identity of a reviewer in a blinded paper review process from the authors but not from the editor) or of privacy (e.g., do not disclose identity of a source without the source's consent). In the past decade there has been significant progress regarding the structure and representation of provenance data as a directed acyclic graph. However, currently there is no overarching, systematic framework for the security and privacy of provenance data and their tradeoffs with respect to the utility of provenance data. The development of such a framework is recognized as one of a handful of promising thrusts in recent reports on Federal game-changing R&D for cyber security, particularly aligned with the theme of Tailored Trustworthy Spaces.This project is to develop a comprehensive technical and scientific framework to address the security and privacy challenges of provenance data, and the attendant tradeoffs, so that our society can gain maximum benefit from applications of provenance data. Detailed foundational research is to be performed on security enhanced data models, access control and usage models, privacy including annonymization and sanitization, integrity, accountability and risk management techniques for provenance data. This foundational research is complemented by data provenance case studies in scientific and cyber security information sharing, and construction of prototype data provenance systems at the operating systems and data layers. Moreover, reference architectures and definitions of corresponding provenance management services are to be defined, identifying how these services can be effectively deployed in enterprises, and developing a risk-management framework to guide application architects, designers and users to effectively embed data provenance in their specific context. The project results will beneficially impact society at large by increasing trustworthiness of data acquired, transmitted and processed by computer systems. From the educational side, both theory and practice of data provenance are to be integrated in the undergraduate and graduate training of students, including underrepresented minority and female students, in all the collaborative institutions of this project.

数据出处是指对象内容的历史及其连续的转换。数据出处的知识对许多目的都是有益的，例如增强数据可信度，促进问责制，验证合规性，有助于取证，并实现更有效的访问和使用控制。 出处数据最少需要完整性保证才能实现这些好处。 此外，出处数据可能需要保密的保证（例如，在盲目的纸质审查过程中保护审稿人的身份，但不会从编辑中获得）或隐私权（例如，未经源的同意，请勿披露来源的身份）。 在过去的十年中，作为定向的无环图，在出处数据的结构和表示方面取得了重大进展。 但是，目前尚无供养数据的安全性和隐私性的总体，系统框架，相对于出处数据的效用。 这种框架的开发被认为是关于联邦改变游戏规则的网络安全研发的最新报道，尤其与量身定制的值得信赖的空间的主题保持一致。该项目旨在开发一个全面的技术和科学框架解决出处数据的安全性和隐私挑战以及随之而来的权衡，以便我们的社会可以从出处数据的应用中获得最大收益。详细的基础研究将在安全增强的数据模型，访问控制和使用模型，包括匿名和消毒，完整性，问责制和风险管理技术（用于出处数据）上进行。这项基础研究与科学和网络安全信息共享中的数据出处案例研究以及操作系统和数据层的原型数据出处系统的构建相辅相成。此外，将要定义相应出处管理服务的参考架构和定义，以确定如何有效地在企业中部署这些服务，并开发一个风险管理框架，以指导应用程序建筑师，设计师和用户有效地嵌入数据出处。语境。该项目的结果将通过增加计算机系统获取，传输和处理的数据的可信赖性来对社会产生重大影响。从教育方面，数据出处的理论和实践都应集成到该项目的所有协作机构中的学生的本科和研究生培训中，包括代表性不足的少数群体和女学生。