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; 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很多机会。大规模收集和挖掘数据的能力继续使新的应用程序并改变我们的生活。但是，隐私问题被视为朝着数据共享和分析的主要障碍。该项目为“遗忘计算”开发了一个实用的框架，该框架允许对敏感数据进行一般计算，但没有披露数据。拟议的工作采用合并的算法和编程语言镜头。在编程语言方面，拟议的工作设计了友好的编程抽象，以促进程序将程序汇编为有效，遗忘的表示。在算法方面，建议的工作设计有效的遗忘算法，以广泛表征现实的任务。最终的框架obLIVM允许非专家开发人员像今天一样编写程序，并且Oblivm会自动生成相应的遗忘计算实现。 Oblivm支持几种不同的架构来实施遗忘的计算，包括基于可信赖的硬件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，以便开发人员可以编写程序，而与后端体系结构无关 - 通过通用中间表示，可以将该程序汇编为基于可信的硬件或基于加密的后端体系结构。 Oblivm可以促进在现实生活中的遗忘计算的部署，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。