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

在过去的十年中，人们对使用大规模集群（如MapReduce和Spark）处理海量数据集越来越感兴趣。这种计算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算法经常在敏感数据上执行，因此必须开发用于高效MPC风格计算的隐私保护技术。该项目探索了一个新的理论基础，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和新的具体有效的安全MPC算法产生新的理论认识。理论重点探讨了在MPC架构上实现高效安全计算所需的设置和加密假设等问题，并了解了轮复杂度、通信效率和其他合适的性能指标。实践推力探索了理论范式发展的具体有效实例，并验证了它们的实际可扩展性。该项目为美国和以色列的教师和学生组织了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代码的努力公开提供。本课题涉及研究生和本科生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.