SaTC: CORE: Small: External Obliviousness in Trusted Execution Environments

SaTC：核心：小：可信执行环境中的外部遗忘

• 批准号: 1815814
• 负责人: Yuzhe Tang
• 金额: $ 49.7万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815814&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; External; Obliviousness

Processing personal data in "the cloud" is an increasingly common computing application. For example, health monitors collect heartbeat data and upload to cloud for personal healthcare and wellbeing. This increases privacy and data security risks. This project aims to achieve provable security in the cloud by protecting data-access patterns in a security-oriented cloud architecture built on the recently released Intel SGX (Software Guard eXtensions) processor. The approach of the project is to combine hardware-level protection for strong processor-memory isolation with algorithm-level protection for data-oblivious computing. This new combination, termed external obliviousness, is intended to enable strongly secure computation at a large data scale. The project consists of two thrusts: 1) building externally oblivious programming support for common computation primitives, 2) building externally oblivious software systems for data-analytical queries.The project will result in multiple broader impacts: The new system-building method of external obliviousness will influence cyber-security and systems research community. The new secure software will potentially expand the cloud industry into supporting security-oriented applications in electronic healthcare. The project will lead to hands-on lab modules integrated with residential curriculum and a nation-wide education platform for both undergraduate students and under-represented groups. The project will generate performance-evaluation results from the developed codebase. They will be published to the online repository and be maintained there for 3 years: https://exo-sgx.github.io.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在“云”中处理个人数据是越来越常见的计算应用。例如，健康监测器收集心跳数据并上传到云，用于个人医疗保健和健康。这增加了隐私和数据安全风险。该项目旨在通过保护基于最近发布的英特尔SGX（Software Guard eXtensions）处理器的面向安全的云架构中的数据访问模式，实现云中可证明的安全性。该项目的方法是将联合收割机硬件级保护与算法级保护相结合，以实现强大的处理器-存储器隔离和数据无关计算。这种新的组合，称为外部遗忘，旨在实现大数据规模的强安全计算。该项目包括两个重点：1）为通用计算原语构建外部遗忘编程支持; 2）为数据分析查询构建外部遗忘软件系统。该项目将产生多个更广泛的影响：外部遗忘的新系统构建方法将影响网络安全和系统研究界。新的安全软件可能会将云计算行业扩展到支持电子医疗保健中面向安全的应用程序。该项目将为本科生和代表性不足的群体提供与住宅课程相结合的实践实验模块和全国范围的教育平台。该项目将从开发的代码库中生成性能评估结果。它们将被发布到在线存储库，并在那里保存3年：https://exo-sgx.github.io.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。