CAREER: Fortifying Leaky Hardware Interfaces with Distinguishability Set Architectures

对个人数据进行计算是一把双刃剑。一方面，它使个性化医疗和疾病预测等革命性的新应用成为可能。另一方面，它存在将上述个人数据泄露给不受欢迎的各方的风险。例如，在今天的处理器芯片上使用个人数据可以通过处理器留下的痕迹来揭示这些数据。更糟糕的是，不同的处理器留下不同的痕迹，揭示不同的信息，这取决于它们是如何设计的。该项目将为现有和未来的处理器芯片开发防止数据通过处理器泄漏的技术。技术方法是为现有和未来的处理器设计可区分集体系结构(DSA)。DSA是现有指令集体系结构(ISA)的对等体。ISA规定了每条指令的功能，而DSA规定了每条指令在什么条件下泄露秘密信息。有了DSA，程序员或编译器可以调整敏感程序，以避免泄露机密。第一个项目将开发DSA基础，回答诸如DSA应该是什么样子以及如何通过各种处理器优化来捕获泄漏等问题。第二个推力将开发使用DSA来提高程序安全性的编译器和硬件。通过准确地描述处理器何时以及如何泄露秘密，DSA将在软件和硬件方面释放创新。在软件方面，程序员可以专注于应用程序，而支持DSA的编译器将这些应用程序转换为适合在不同处理器上运行的安全变体。在硬件方面，架构师可以使用DSA来推断硬件优化对隐私的影响。该项目将培训一批新的学生和研究人员，他们可以跨越正式规范、微体系结构和编译器来构建安全系统，并在未来将所学到的经验应用于其他与隐私相关的问题。DSA项目将在项目结束后在伊利诺伊大学托管至少3年的面向公众的网站上存储所有出版物、代码和数据集。这些信息将通过商业网站提供。到这些网站的链接将反映在http://cwfletcher.net/dsa.This奖上，该奖项反映了国家科学基金会的法定使命，并已被认为值得支持，通过使用基金会的智力优势和更广泛的影响审查标准进行评估。

Computing on personal data is a double-edged sword. On one hand, it enables revolutionary new applications such as personalized medicine and disease prediction. On the other hand, it runs the risk of revealing said personal data to unwanted parties. For example, using personal data on today’s processor chips can reveal that data through traces that the processor leaves behind. To make matters worse, different processors leave behind different traces, revealing different information, depending on how they were designed. This project will develop techniques to prevent data leakage through processors, for existing and future processor chips.The technical approach is to design a Distinguishability Set Architecture (DSA) for existing and future processors. DSAs are peers to existing Instruction Set Architectures (ISAs). Whereas the ISA specifies the functionality of each instruction, the DSA specifies under what conditions each instruction reveals secret information. With a DSA, programmers or compilers can tune sensitive programs to avoid leaking secrets. The first project thrust will develop DSA foundations, answering questions such as what should a DSA look like and how to capture leakage through various processor optimizations. The second thrust will develop compilers and hardware that use DSAs to improve program security.By precisely describing when and how processors reveal secrets, DSAs will unlock innovation on both software and hardware fronts. On the software side, programmers can focus on applications while DSA-aware compilers translate those applications to secure variants fit to run on different processors. On the hardware side, architects can use DSAs to reason about the privacy implications of hardware optimizations. The project will train a new class of students and researchers who can work across formal specifications, micro-architecture and compilers to build secure systems and, in the future, apply the lessons learned to other privacy-related problems.The DSA project will store all publications, code, and data-sets on public-facing websites, hosted at the University of Illinois for at least 3 years after the end of the project. This information will be made available via commercial websites. Links to these websites will be mirrored at http://cwfletcher.net/dsa.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.