CAREER: Architectural Foundations for Practical Privacy-Preserving Computation

为了获得高质量的在线服务，用户必须牺牲隐私，将他们的私人、敏感数据上传到云中进行处理。这迫使用户做出选择：要么让他们的数据保持隐私，要么实现强大的在线服务的效用。隐私保护计算是一种新兴的计算范式，它支持直接对加密数据进行计算。它有可能打破隐私效用之间的权衡。今天，隐私保护计算没有得到广泛应用，因为它的计算成本太高，不切实际。以前的工作一直报告说，在隐私保护计算下运行的程序比没有隐私保护计算的程序慢4-6个数量级。该项目将开发新的硬件和软件技术，以显著改进隐私保护计算的处理。这些方法将把隐私保护计算加速到实用水平，为用户提供无与伦比的隐私保障，同时提供对现已成为日常生活不可或缺的在线服务的访问。为了促进这种新的计算范式的采用和继续发展，该项目还将开发教育材料，在所有级别教授这些新主题，包括K-12课程、大学课堂和会议教程。克服极端的减速需要在整个计算堆栈中进行优化。本项目采用的方法深深植根于协同设计，同时考虑了硬件、软件和算法的相互作用。一个关键的观察是，隐私保护计算自然是数据无关的，这意味着所有程序行为在编译时都是静态知道的。有鉴于此，硬件和软件之间的仔细协调可以实现实际隐私保护计算所需的极大程度的加速。该项目将考虑两种(两种)隐私保护计算，以有效地支持所有计算：算术(例如，同态加密)和布尔(例如，乱码电路)。对于同态加密，除了用于高性能映射的数据流优化外，还将探索和开发基于脉动阵列的体系结构来加速计算。接下来，该项目将设计出进一步加速布尔计算的方法。该项目将开发一种基于芯片的体系结构，以打破单片设计的不可伸缩结构，并结合程序分区算法，高效地在芯片上并行工作。最后，该项目将构建编译器基础设施，以应对有效地将明文程序映射到隐私保护计算基元的挑战。通过该奖项取得的进展将为私人计算时代的处理奠定基础。该奖项反映了NSF的法定使命，并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，被认为值得支持。

To access high-quality online services users must sacrifice privacy and upload their private, sensitive data to the cloud for processing. This forces users to make a choice: keep their data private or realize the utility of powerful online services. Privacy-preserving computation is an emerging computational paradigm that supports computation directly on encrypted data. It has the potential to break the privacy-utility tradeoff. Today, privacy-preserving computation is not widely used as it is far too computationally expensive to be practical. Prior work has consistently reported that programs running under privacy-preserving computation are 4-6 orders of magnitude slower than without it. This project will develop novel hardware and software techniques to substantially improve the processing of privacy-preserving computation. The methods will speedup privacy-preserving computation to a practical level, providing users unparalleled privacy guarantees while simultaneously providing access to online services now integral to everyday life. To facilitate the adoption and continued advancement of this new computational paradigm, this project will also develop educational material to teach these new topics at all levels including K-12 programs, university classrooms, and conference tutorials.Overcoming the extreme slowdown requires optimizations across the computational stack. The approach taken in this project is deeply rooted in co-design, considering the interplay of hardware, software, and algorithms together. A key observation is that privacy-preserving computation is naturally data oblivious, implying that all program behavior is known statically at compile time. Given this, careful orchestration between hardware and software can enable the extreme degrees of speedup required for practical privacy-preserving computation. The project will consider two (both) varieties of privacy-preserving computation to efficiently support all computation: arithmetic (e.g., homomorphic encryption) and Boolean (e.g., garbled circuits). For homomorphic encryption, systolic array based architectures will be explored and developed to accelerate computation in addition to dataflow optimizations for high-performance mapping. Next, the project will devise methods to further speedup Boolean computations. The project will develop a chiplet-based architecture to break the non-scalable structures of monolithic designs alongside program partitioning algorithms that parallelize work across chiplets efficiently. Finally, the project will build compiler infrastructure to address the challenges of efficiently mapping plaintext programs to privacy-preserving computation primitives. The advances made via this award will lay the foundations for processing in the era of private computing.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.