Multi-protocol mixing for multi-party computation
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Authors
Fogg, Brandon
Issue Date
2025-08
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Computer science
Alternative Title
Abstract
Multi-party computation (MPC) is a rapidly developing field of Computer Science and is approaching practical deployment; there are numerous backend implementations and protocol variants available for research use. This leaves users with a multitude of both backends and protocols to perform secure computation. Current backends give the user access to different MPC design paradigms as well as to different data representations. The choice of which MPC protocol is most efficient depends heavily on the computation being performed and requires the user to have extensive backend and protocol knowledge. Protocol Mixing is an optimization strategy that assigns protocols to computations in a way that maximizes the benefits of each protocol throughout an input program. The introduction of mixing algorithms would allow both experienced and new users to access the fastest possible execution times for their programs without needing to learn each backend and protocol they use. We present a toolchain which provides an automatic solution to the Optimal $n$-Protocol Assignment problem, enabling developers to focus on designing their application logic without having to manage protocol selection and without requiring extensive knowledge of each backend. Our methodology comes with a provable guarantee; assuming structural properties which most practical programs meet, we show that the Optimal Protocol Assignment problem is tractable and provide a solution which demonstrates this claim. Our method leverages properties of SSA-form programs to divide the computation into manageable blocks, assign protocols to each statement within these blocks, discard provably sub-optimal blocks, and compute a merge schedule to yield a globally optimal set of assignments. Finally, we present results for a two-protocol and three-protocol backend (MP-SPDZ and MOTION respectively) for a standard set of benchmarks from protocol mixing literature. Notably, our solver produced assignments for Biometric Matching which were more efficient than the native mixer of MP-SPDZ, highlighting our mixer's ability to understand the global semantics of a program over locally optimal decisions.
Description
August2025
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY