STARSS: Small: GC@Scale: Synthesis, optimization, and implementation of Garbled Circuits for Scalable Privacy-Preserving Computing

STARSS：小型：GC@Scale：用于可扩展隐私保护计算的乱码电路的综合、优化和实现

• 批准号: 1619261
• 负责人: Farinaz Koushanfar
• 金额: $ 29.82万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619261&HistoricalAwards=false
• 关键词: STARSS; Small; GC; Scale; Synthesis

Computing on sensitive data is a standing challenge central to several modern-world applications. Secure Function Evaluation (SFE) allows mistrusting parties to jointly compute an arbitrary function on their private inputs without revealing anything but the result. The GC@Scale project focuses on novel scalable methods for addressing SFE, which directly translate to stronger cryptography and security for myriads of tasks with sensitive data. The applications are wide reaching and include privacy-preserving processing of medical, genome, and biometric data, as well as personal, government, and industrial cloud computing. The project includes an ambitious educational program that targets both undergraduate/ graduate students, and also addresses issues related to outreach.The concept of SFE using Garbled Circuits (GC) was introduced by Yao. Despite a decade of research in GC implementation and several key progresses, scalability of the available methods has been hampered by the circuit representation as a directed acyclic graph, and software-level local logic optimizations. GC@Scale leverages PI's recent work, which has changed the SFE landscape by viewing GC generation as an atypical sequential logic synthesis. The project plans to advance the understanding and enable expanded exploration of SFE methodologies, while simultaneously enriching the theory, practice, and tools for logic design, synthesis, mapping and optimization. The proposed plan includes: (i) design and FPGA implementation of an efficient general purpose Garbled Processor for secure computation; (ii) Creating the challenging application-specific GC matching and search engines with a higher than linear complexity. (iii) Devising new custom SFE engines for Machine Learning tasks.

对敏感数据的计算是几个现代世界应用程序的核心挑战。安全函数评估（Secure Function Evaluation， SFE）允许互不信任的各方在各自的私有输入上共同计算任意函数，而无需透露除结果之外的任何内容。GC@Scale项目专注于解决SFE的新颖可扩展方法，这直接转化为具有敏感数据的无数任务的更强的加密和安全性。应用范围广泛，包括医疗、基因组和生物特征数据的隐私保护处理，以及个人、政府和工业云计算。该项目包括一个雄心勃勃的教育计划，针对本科生和研究生，并解决与外展相关的问题。使用乱码电路（GC）的SFE概念是由Yao提出的。尽管在GC实现方面进行了十年的研究并取得了一些关键进展，但现有方法的可扩展性一直受到电路表示为有向无环图和软件级局部逻辑优化的阻碍。GC@Scale利用PI最近的工作，该工作通过将GC生成视为非典型的顺序逻辑合成而改变了SFE的格局。该项目计划促进对SFE方法的理解和扩展探索，同时丰富逻辑设计、综合、映射和优化的理论、实践和工具。提出的计划包括：(i)设计和FPGA实现一种高效的通用乱码处理器，用于安全计算；（ii）创建具有挑战性的特定于应用程序的GC匹配和搜索引擎，其复杂性高于线性。（iii）为机器学习任务设计新的定制SFE引擎。