CAREER: Architectural Foundations for Practical Privacy-Preserving Computation

职业：实用隐私保护计算的架构基础

• 批准号: 2340137
• 负责人: Brandon Reagen
• 金额: $ 50万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2029-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340137&HistoricalAwards=false
• 关键词: CAREER; Architectural; Foundations; Practical; Privacy

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.

要访问高质量的在线服务，用户必须牺牲隐私并将其私人敏感数据上传到云中进行处理。这迫使用户做出选择：保持其数据私密或实现强大的在线服务的实用性。隐私计算是一种新兴的计算范式，它直接支持加密数据的计算。它有可能打破隐私性权衡的权衡。如今，保存隐私的计算并未被广泛使用，因为它在计算上太昂贵而无法实用。先前的工作始终报告说，在隐私权计算下运行的程序比没有它的速度慢4-6个数量级。该项目将开发新颖的硬件和软件技术，以大大改善隐私保护计算的处理。这些方法将将隐私的计算加速到实用层面，从而为用户提供无与伦比的隐私保证，同时提供对在线服务的访问，而现在则是日常生活中不可或缺的一部分。为了促进这种新的计算范式的采用和持续发展，该项目还将开发教育材料，以教授这些新主题，包括K-12计划，大学教室和会议教程。考虑到硬件，软件和算法之间的相互作用，该项目中采用的方法根深蒂固。一个关键的观察结果是，保护隐私的计算是自然而然的数据，这意味着所有程序行为在编译时静态已知。鉴于此，硬件和软件之间的精心编排可以实现实用隐私计算所需的极端速度。该项目将考虑两种（两个）的隐私计算品种，以有效地支持所有计算：算术（例如，同构加密）和布尔值（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收缩期阵列的架构以加速计算。接下来，该项目将设计方法来进一步加速布尔计算。该项目将开发一种基于chiplet的体系结构，以打破整体设计的不可缩放结构，并在程序分配算法的同时，有效地使整个chiplet的工作都并行。最后，该项目将构建编译器基础架构，以解决有效地将明文程序映射到隐私计算原始原始原始基底的挑战。通过该奖项取得的进步将为私人计算时代的加工奠定基础。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被视为值得通过评估的支持。