Secure Multi-Party Computation (MPC) allows multiple parties to perform privacy-preserving computation on their secret data. MPC protocols based on secret sharing have high throughput which makes them well-suited for batch processing, where multiple instances are evaluated in parallel. So far, practical implementations of secret sharing-based MPC protocols mainly focus on runtime and communication efficiency, so the memory overhead of protocol implementations is often overlooked. Established techniques to reduce the memory overhead for constant-round garbled circuit protocols cannot be directly applied to secret sharing-based protocols because they would increase the round complexity. Additionally, state-of-the-art implementations of secret sharing-based MPC protocols are implemented in C/C++ and maybe exhibit memory unsafety and memory leaks which could lead to undefined behavior. In this paper, we present SEEC: SEEC Executes Enormous Circuits, a framework for secret sharing-based MPC with a novel approach to address memory efficiency and safety without compromizing on runtime and communication efficiency. We realize SEEC in Rust, a language known for memory-safety at close-to-native speed. To reduce the memory footprint, we develop an in-memory representation for sub-circuits. Thus, we never inline sub-circuit calls during circuit evaluation, a common issue that blows up memory usage in MPC implementations. We compare SEEC with the state-of-the-art secret sharing-based MPC frameworks ABY (NDSS'15), MP-SPDZ (CCS'20), and MOTION (TOPS'22) w.r.t. runtime, memory, and communication efficiency. Our results show that our reliable and memory-safe implementation has competitive or even better performance.

Secure Multi-Party Computation (MPC) is continuously becoming more and more practical. Many optimizations have been introduced, making MPC protocols more suitable for solving real-world problems. However, the MPC protocols and optimizations are usually implemented as a standalone proof of concept or in an MPC framework and are tightly coupled with special-purpose circuit formats, such as Bristol Format. This makes it very hard and time-consuming to re-use algorithmic advances and implemented applications in a different context. Developing generic algorithmic optimizations is exceptionally hard because the available MPC tools and formats are not generic and do not provide the necessary infrastructure.