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

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.