NSF-BSF: SaTC: CORE: Small: Secure Massively Parallel Computations: Foundations and Constructions

NSF-BSF：SaTC：核心：小型：安全大规模并行计算：基础和结构

• 批准号: 2044679
• 负责人: Elaine Shi
• 金额: $ 35万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044679&HistoricalAwards=false
• 关键词: NSF; BSF; SaTC; CORE; Small

Over the past decade, there has been increasing interest in processing enormous data-sets using large-scale clusters such as MapReduce and Spark. The most widely-accepted theoretical abstraction of such computations is called the Massively Parallel Computation (MPC) model. Since MPC algorithms are often executed on sensitive data, it is imperative to develop privacy-preserving techniques for efficient MPC-style computations. The project explores a new theoretical foundation that enables efficient secure computation on MPC frameworks, as well as efficient implementations. The solutions developed provide privacy-preserving, large-scale data analytics without leaking private information. The project combines cryptographic and algorithms techniques, and is expected to lead to new theoretical understanding of secure MPC and new concretely efficient secure MPC algorithms. The theoretical thrust explores questions such as the setup and cryptographic assumptions needed for realizing efficient secure computation on an MPC architecture, and understanding the round complexity, communication efficiency, and other suitable performance metrics. The practical thrust explores concretely efficient instantiations of the theoretical paradigms developed, and validates their practical scalability. The project organizes joint workshops on cybersecurity for faculty and students in the US and Israel. Software tools developed in the research project are made publicly available through open source efforts. The project involves graduate and undergraduates students in this research.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.

在过去的十年中，人们越来越关注使用MapReduce和Spark等大规模集群来处理庞大的数据集。这种计算的最广泛接受的理论抽象被称为大规模并行计算（MPC）模型。 由于MPC算法通常在敏感数据上执行，因此必须开发用于高效MPC式计算的隐私保护技术。该项目探索了一种新的理论基础，可以在MPC框架上实现高效的安全计算，以及高效的实现。开发的解决方案提供了隐私保护，大规模数据分析，而不会泄露私人信息。该项目结合了密码学和算法技术，有望对安全MPC产生新的理论认识和新的具体有效的安全MPC算法。 理论上的推力探讨了诸如在MPC架构上实现高效安全计算所需的设置和加密假设，以及理解轮复杂度，通信效率和其他合适的性能指标等问题。实践的推力探索具体有效的理论范式的实例开发，并验证其实际的可扩展性。该项目为美国和以色列的教师和学生组织网络安全联合研讨会。在研究项目中开发的软件工具通过开放源码努力公开提供。这个奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。