CAREER: An Automated Compiler-Runtime Framework for Democratizing Secure Collaborative Computation

最近高级分析和机器学习领域的革命促使组织对高价值数据的需求日益增长。然而，在许多领域获取足够数量的此类数据是具有挑战性的，因为大多数高价值数据都是敏感的，并且由多个组织拥有和拆分。由于隐私问题、监管政策和/或业务竞争，组织通常无法共享其拥有的数据。一个有前途的解决方案是安全多方计算(MPC)，这是一种密码技术，允许组织在其联合数据集上运行复杂的计算，而不会向其他方泄露敏感输入。不幸的是，开发高效的协议仍然是一个需要密码专业知识的劳动密集型过程，对于大多数用户和开发人员来说是遥不可及的。该项目更广泛的意义和重要性在于，通过设计和构建端到端的、集成的编译器运行时框架来自动生成和执行优化的、特定于工作负载的MPC协议，从而加速和推广MPC的采用。该项目的创新之处在于通过开发和结合来自编译器、密码学和分布式系统的新见解来构建这样一个框架。研究团队计划从三个方面来解决这一研究问题。第一个推力通过支持可扩展到各种MPC原语的基本编译基础设施来开发平台的基础。第二个推力为编译器设计了新的技术，以便为用户提供的高级程序生成特定于工作负载的MPC协议。第三个推力创建了一个新的分布式运行时，用于有效地执行和调度MPC协议，这些协议是使用推力2中的技术生成的。该团队正计划为其他研究人员和教育工作者开源该框架，并正在努力创建新的研究生课程和扩展计划，以使本科生更容易获得研究。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。

The recent revolution in advanced analytics and machine learning gave rise to a growing demand among organizations for high-value data. However, obtaining sufficient volumes of such data is challenging in many areas because most high-value data is sensitive, and is owned by and split across multiple organizations. Organizations often cannot share the data they own due to privacy concerns, regulatory policies, and/or business competition. A promising solution is secure multiparty computation (MPC), a cryptographic technique that allows organizations to run complex computations on their joint dataset without revealing sensitive inputs to other parties. Unfortunately, developing efficient protocols remains a labor-intensive process that requires cryptographic expertise and is out of reach for most users and developers. The project's broader significance and importance are to accelerate and democratize the adoption of MPC by designing and building an end-to-end, integrated compiler-runtime framework that automatically generates and executes optimized, workload-specific MPC protocols. The project’s novelties are to build such a framework by developing and combining new insights from compilers, cryptography, and distributed systems. The team of researchers plan to address the research question in three broad thrusts. The first thrust develops the foundation of the platform by supporting a basic compilation infrastructure that is extensible to a wide variety of MPC primitives. The second thrust designs novel techniques for the compiler to generate workload-specific MPC protocols for user-provided high-level programs. The third thrust creates a new distributed runtime for efficiently executing and scheduling MPC protocols that are generated using techniques from Thrust 2. The team is planning to open source the framework for other researchers and educators, and is working to create new graduate courses and outreach programs to make research more accessible to undergraduate students.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.