CAREER: Practical Oblivious Computation

职业：实用的遗忘计算

• 批准号: 1453634
• 负责人: Elaine Shi
• 金额: $ 49万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453634&HistoricalAwards=false
• 关键词: CAREER; Practical; Oblivious; Computation

The explosion of digital data carries a wealth of opportunities. The ability to collect and mine data at scale continues to enable new applications and transform our lives. However, privacy concerns are seen as a major hurdle towards data sharing and analytics. This project develops a practical framework for "oblivious computation", which allows general computation over sensitive data, but without disclosing the data. The proposed work takes a combined algorithms and programming language lens. On the programming language side, the proposed work designs friendly programming abstractions that facilitate the compilation of programs into efficient, oblivious representations. On the algorithms side, the proposed work designs efficient oblivious algorithms for a wide characterization of realistic tasks. The resulting framework, ObliVM, allows non-expert developers to write programs much as they do today, and ObliVM automatically generates the corresponding oblivious computation implementation. ObliVM supports several different architectures for implementing oblivious computation, including those based on trusted hardware and those based on cryptographic secure computation. ObliVM offers a unified programming framework such that a developer can write a program irrespective of the backend architecture -- through a universal intermediate representation, this program can be compiled to either a trusted hardware-based or a cryptography-based backend architecture. ObliVM can facilitate the deployment of oblivious computation in real-life scenarios, and allow businesses and individuals to monetize sensitive data without the risk of exposure.

数字数据的爆炸带来了大量的机会。大规模收集和挖掘数据的能力将继续推动新的应用并改变我们的生活。然而，隐私问题被视为数据共享和分析的主要障碍。该项目开发了一个实用的“不经意计算”框架，它允许对敏感数据进行一般计算，但不会泄露数据。提出的工作采取了结合算法和编程语言的透镜。在编程语言方面，所提出的工作设计友好的编程抽象，促进程序编译成高效的，不经意的表示。在算法方面，所提出的工作设计了有效的遗忘算法的广泛表征的现实任务。由此产生的框架，ObliVM，允许非专家开发人员编写程序，就像他们今天做的那样，ObliVM自动生成相应的不经意计算实现。ObliVM支持多种不同的架构来实现不经意计算，包括基于可信硬件的架构和基于加密安全计算的架构。ObliVM提供了一个统一的编程框架，这样开发人员就可以编写一个程序，而不管后端架构如何--通过一个通用的中间表示，这个程序可以被编译为基于可信硬件或基于密码的后端架构。ObliVM可以促进在现实生活场景中部署不经意计算，并允许企业和个人在没有暴露风险的情况下将敏感数据货币化。