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.

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and been widely applied in a variety of applications, including FPGAs, image processing, and database management systems. In cryptography, using such LUTs instead of conventional gates like AND and XOR results in more compact circuits and has been shown to substantially improve online performance when evaluated with secure multi-party computation. Several recent works on secure floating-point computations and privacy-preserving machine learning inference rely heavily on existing LUT techniques. However, they suffer from either large overhead in the setup phase or subpar online performance.We propose FLUTE, a novel protocol for secure LUT evaluation with good setup and online performance. In a two-party setting, we show that FLUTE matches or even outperforms the online performance of all prior approaches, while being competitive in terms of overall performance with the best prior LUT protocols. In addition, we provide an open-source implementation of FLUTE written in the Rust programming language, and implementations of the Boolean secure two-party computation protocols of ABY2.0 and silent OT. We find that FLUTE outperforms the state of the art by two orders of magnitude in the online phase while retaining similar overall communication.