# NFGen: Automatic Non-linear Function Evaluation Code Generator for General-purpose MPC Platforms

arXiv: https://arxiv.org/abs/2210.09802 · HTML: https://mlsanity.com/paper/2210.09802/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [East: Efficient and Accurate Secure Transformer Framework for Inference](https://mlsanity.com/paper/2308.09923/) (2023)
- [Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference](https://mlsanity.com/paper/2405.17485/) (2024)
- [Compact: Approximating Complex Activation Functions for Secure Computation](https://mlsanity.com/paper/2309.04664/) (2023)
- [Spin: An Efficient Secure Computation Framework with GPU Acceleration](https://mlsanity.com/paper/2402.02320/) (2024)
- [CryptoTensors: A Light-Weight Large Language Model File Format for Highly-Secure Model Distribution](https://mlsanity.com/paper/2512.04580/) (2025)

## Abstract (arXiv)

Due to the absence of a library for non-linear function evaluation, so-called general-purpose secure multi-party computation (MPC) are not as ''general'' as MPC programmers expect. Prior arts either naively reuse plaintext methods, resulting in suboptimal performance and even incorrect results, or handcraft ad hoc approximations for specific functions or platforms. We propose a general technique, NFGen, that utilizes pre-computed discrete piecewise polynomials to accurately approximate generic functions using fixed-point numbers. We implement it using a performance-prediction-based code generator to support different platforms. Conducting extensive evaluations of 23 non-linear functions against six MPC protocols on two platforms, we demonstrate significant performance, accuracy, and generality improvements over existing methods.