Paper 2026/261

Logarithmic-Depth Pseudorandom Functions from Well-Founded Code-Based Assumptions

Aayush Jain, Carnegie Mellon University

Ilan Komargodski, Hebrew University of Jerusalem

Abstract

We give the first $\mathsf{NC}^1$-computable Pseudorandom Function (PRF) constructions from well-founded (non-ad-hoc) code-based assumptions. Specifically, we give two constructions based on two different classical variants of the learning parity with noise (LPN) assumption: (1) An $\mathsf{NC}^1$-computable PRF from hardness of Sparse-LPN [Alekhnovich, FOCS~'03] (with respect to a sublinear-depth expander graph family). This PRF is also key-homomorphic. (2) An $\mathsf{NC}^1$-computable PRF from hardness of Ring-LPN [Heyse et al., FSE~'12]. Both of these assumptions have been studied for many years in cryptography and average-case complexity. Ring-LPN has been used in the context of silent preprocessing for MPC and has a stable cryptanalysis. The study of counterexamples for Sparse-LPN is an active area of research. Notably, within the range of parameters that we need, none of these assumptions is known to imply collision-resistant hashing, and the first is not known to imply public-key encryption. Prior constructions of code-based PRFs (let alone key-homomorphic) are either super-logarithmic depth, or rely on newly introduced assumptions. As a bonus, we give a similar result relying on the \emph{classical} LPN assumption, albeit with quasi-polynomial hardness: -A key-homomorphic $\mathsf{NC}^1$-computable PRF from quasi-polynomial hardness of the classical LPN [Blum et al., CRYPTO~'93]. Technically, all of our results are obtained via a refinement and substantial extension of the recent ideas of [Ding, Jain, and Komargodski, STOC~'25].

@misc{cryptoeprint:2026/261,
      author = {Youlong Ding and Aayush Jain and Ilan Komargodski},
      title = {Logarithmic-Depth Pseudorandom Functions from Well-Founded Code-Based Assumptions},
      howpublished = {Cryptology {ePrint} Archive, Paper 2026/261},
      year = {2026},
      url = {https://eprint.iacr.org/2026/261}
}