Paper 2026/750

MCU: Algebraic and Transcendental Function Evaluation in MPC without Preprocessing

Abstract

The rapid proliferation of privacy-sensitive machine learning and scientific computing applications has intensified the demand for secure multi-party computation (MPC) protocols that can efficiently evaluate complex nonlinear functions. Despite four decades of research, MPC systems face two fundamental barriers: heavy offline preprocessing and limited support for transcendental functions. Existing preprocessing-free frameworks are confined to comparison-related functions (e.g., Bicoptor) and cannot handle exponentials or trigonometric functions. In this paper, we propose MCU (Mask-Compute-Unmask), a novel MPC architecture that directly supports a broad class of algebraic and transcendental functions without any offline preprocessing. MCU introduces a semi-honest, non-colluding helper party (HP) as an active computational engine: parties additively mask their inputs, the HP computes the target function on the aggregated masked data, and the parties then unmask using locally known masks. This paradigm enables constant-round, scalable protocols for general multiplication (2 rounds), power functions (2 rounds), exponentials (8 rounds), and trigonometric functions (2 rounds). All protocols achieve simulation-based security and rely solely on synchronized pseudorandom generators—no preprocessing, no polynomial approximations. To the best of our knowledge, MCU is the first MPC framework that simultaneously achieves preprocessing-free, constant-round, exact evaluation of exponentials and trigonometric functions—functions essential for scientific computing, differential privacy mechanisms, and advanced neural network architectures. Our multiplication protocol scales better than Beaver-based multiplication in multi-party settings, and our exponential and trigonometric protocols achieve sub-ULP accuracy. The rapid proliferation of privacy-sensitive machine learning and scientific computing applications has intensified the demand for secure multi-party computation (MPC) protocols that can efficiently evaluate complex nonlinear functions. Despite four decades of research, MPC systems face two fundamental barriers: heavy offline preprocessing and limited support for transcendental functions. Existing preprocessing-free frameworks are confined to comparison-related functions (e.g., Bicoptor) and cannot handle exponentials or trigonometric functions. In this paper, we propose MCU (Mask-Compute-Unmask), a novel MPC architecture that directly supports a broad class of algebraic and transcendental functions without any offline preprocessing. MCU introduces a semi-honest, non-colluding helper party (HP) as an active computational engine: parties additively mask their inputs, the HP computes the target function on the aggregated masked data, and the parties then unmask using locally known masks. This paradigm enables constant-round, scalable protocols for general multiplication (2 rounds), power functions (2 rounds), exponentials (8 rounds), and trigonometric functions (2 rounds). All protocols achieve simulation-based security and rely solely on synchronized pseudorandom generators—no preprocessing, no polynomial approximations. To the best of our knowledge, MCU is the first MPC framework that simultaneously achieves preprocessing-free, constant-round, exact evaluation of exponentials and trigonometric functions—functions essential for scientific computing, differential privacy mechanisms, and advanced neural network architectures. Our multiplication protocol scales better than Beaver-based multiplication in multi-party settings, and our exponential and trigonometric protocols achieve sub-ULP accuracy. To the best of our knowledge, MCU is the first MPC framework that simultaneously achieves preprocessing-free, constant-round, exact evaluation of exponentials and trigonometric functions—functions essential for scientific computing, differential privacy mechanisms, and advanced neural network architectures. Our multiplication protocol scales better than Beaver-based multiplication in multi-party settings, and our exponential and trigonometric protocols achieve sub-ULP accuracy.

Note: Thanks to technique in paper privaLean (eprint.iacr.org/2026/554), our trigonometric functions now is of 2 rounds.