(2026-04) MBU; Scalable and Constant-Round Evaluation of Non-linear Functions in Standard MPC Setting
2026-04-18
After more than four decades of research, multi-party computation (MPC) has achieved remarkable success in handling 2-variable multiplication and comparison-based functions (e.g., ReLU) with practical efficiency. However, for general non-linear functions—such as multiplication of many variables, power, exponential, trigonometric functions, sigmoid, softmax, and GeLU—no native MPC algorithm exists as Beaver-based mutiplication that is constant-round, scalable and exact. Existing solutions rely on either polynomial approximations (trading precision for efficiency), iterative multi-round protocols like Multiplication-to-Addition (M2A) conversion (requiring (\log_2 k) rounds for (k) parties), or Function Secret Sharing (FSS) with lookup tables (introducing quantization errors and large storage, mainly limited to 2-4 parties). These approaches suffer from fundamental trade-offs among accuracy, communication rounds, and scalability.
In this work, we propose a unified \emph{mask-broadcast-unmask} design pattern that enables constant-round, scalable and \emph{approximation-free} evaluation of a wide range of non-linear functions. Our contributions include: \begin{itemize} \item A \textbf{general multiplication} protocol for (k) variables in \emph{one round} with optimal (O(kn)) communication. When (k=2), it reduces to the classic Beaver triple multiplication; when each secret has only one non-zero share and (k=2), it becomes the well-known M2A protocol. \item \textbf{Power functions} ((x^k)) in one round. \item \textbf{Trigonometric functions} ((\sin x, \cos x)) and \textbf{exponential functions} ((a^x)) in 4 rounds. \item \textbf{Sigmoid, softmax} in 6 rounds. \end{itemize} All these protocols are provably secure in the semi-honest model, support arbitrary number of parties, introduce \textbf{no approximation error} beyond plaintext floating-point rounding, and require only constant communication rounds (1–6) independent of function complexity. Furthermore, by restricting the random mask to a suitable range (e.g., ), we can reduce the rounds from 1-6 to 1-3. This work fills the long-standing gap for general non-linear functions in standard MPC settings, making privacy-preserving machine learning more practical for modern DNNs.