CAREER: Practical Oblivious Computation

数字数据的爆炸式增长带来了大量的机会。大规模收集和挖掘数据的能力继续支持新的应用程序并改变我们的生活。然而，隐私问题被视为数据共享和分析的主要障碍。这个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。提出的工作采取了算法和编程语言相结合的镜头。在编程语言方面，建议的工作设计了友好的编程抽象，有助于将程序编译成高效的、不经意的表示。在算法方面，所提出的工作设计了高效的不经意算法，以广泛地刻画现实任务。由此产生的框架ObliVM允许非专业开发人员编写程序，就像他们现在所做的那样，并且ObliVM自动生成相应的不经意的计算实现。ObliVM支持几种实现不经意计算的不同体系结构，包括基于可信硬件的体系结构和基于密码安全计算的体系结构。ObliVM提供了统一的编程框架，使得开发人员可以编写与后端体系结构无关的程序--通过通用中间表示法，该程序可以编译为基于可信硬件或基于密码的后端体系结构。ObliVM可以在现实生活场景中促进不经意计算的部署，并允许企业和个人将敏感数据货币化，而不会有暴露的风险。

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.