CAREER: Pushing the Practicality of Secure Multiparty Computation

Advances in data-driven technologies offer benefits through the centralized collection of user data. However, data centralization and sharing also raise critical security and privacy needs. Secure multi-party computation (MPC) allows a set of parties to compute any function jointly and reveals nothing but the output. It is a crucial tool to enable private data computation without sharing it. This project focuses on further pushing the practicality of MPC protocols. The project's novelties are identifying the hurdles of deploying MPC protocols in real-world settings and designing efficient, robust, and scalable protocols to overcome these issues. The project's broader significance and importance are two-fold: 1) by pushing the use of MPC, the project enables new beneficial applications without sacrificing privacy; 2) the project involves educational and outreach activities to enhance MPC education for students at different stages, improve social welfare, and promote diversity of participation. This project focuses on three aspects of bringing MPC to practice. The first aspect is on secure two-party computation protocols in the malicious setting as efficient and scalable as their semi-honest counterparts. Second, the project investigates MPC efficiency when used for big data in application-specific settings and improved RAM-based MPC protocols for real-world workloads. Third, the project develops new classes of protocols that can scale MPC to hundreds of parties and compute huge statements even when the adversary can corrupt many participants. Finally, the research outcomes from this award are applied and evaluated via academic collaboration and industry deployment. 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.