Paper accepted in ACM TOPS
Success for CROSSING researchers from the ENCRYPTO group
- MOTION – A Framework for Mixed-Protocol Multi-Party Computation
by Lennart Braun (Aarhus University), Daniel Demmler (Universität Hamburg), Thomas Schneider (TU Darmstadt), and Oleksandr Tkachenko (TU Darmstadt)
has been accepted for publication in ACM TOPS.
We present MOTION, an efficient and generic open-source framework for mixed-protocol secure multi-party computation (MPC). MOTION is built in a user-friendly, modular, and extensible way, intended to be used as a tool in MPC research and to increase adoption of MPC protocols in practice. Our framework incorporates several important engineering decisions such as full communication serialization, which enables MPC over arbitrary messaging interfaces and removes the need of owning network sockets. MOTION also incorporates several performance optimizations that improve the communication complexity and latency, e.g., 2× better online round complexity of precomputed correlated Oblivious Transfer (OT).
We instantiate our framework with protocols for N parties and security against up to N−1 passive corruptions: the MPC protocols of Goldreich-Micali-Wigderson (GMW) in its arithmetic and Boolean version and OT-based BMR (Ben-Efraim et al., CCS’16), as well as novel and highly efficient conversions between them, including a non-interactive conversion from BMR to arithmetic GMW.
MOTION is highly efficient, which we demonstrate in our experiments. Compared to secure evaluation of AES-128 with N=3 parties in a high-latency network with OT-based BMR, we achieve a 16× better throughput of 16 AES evaluations per second using BMR. With this, we show that BMR is much more competitive than previously assumed. For N=3 parties and full-threshold protocols in a LAN, MOTION is 10×–18× faster than the previous best passively secure implementation from the MP-SPDZ framework, and 190×–586× faster than the actively secure SCALE-MAMBA framework. Finally, we show that our framework is highly efficient for privacy-preserving neural network inference.