A New Approach for LPN-Based Pseudorandom Functions: Low-Depth and Key-Homomorphic

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Abstract

We give new constructions of pseudorandom functions (PRFs) computable in NC1 from (variants of the) Learning Parity with Noise (LPN) assumption. Prior to our work, the only NC1-computable PRF from LPN-style assumptions was due to Boyle et al. (FOCS 2020) who constructed a weak PRF from a new heuristic variant of LPN called variable-density LPN. We give the following results: (1) A weak PRF computable in NC1 from standard LPN, (2) A (strong) encoded-input PRF (EI-PRF) computable in NC1 from sparse LPN (An EI-PRF is a PRF whose input domain is restricted to an efficiently sampleable and recognizable set. The input encoding can be computed in NC1+ϵ for any constant ϵ > 0, implying a strong PRF computable in NC1+ϵ), and (3) A (strong) PRF computable in NC1 from a (new, heuristic) seeded LPN assumption. In our assumption, each column of the public LPN matrix is generated by an n-wise independent distribution. Supporting evidence for the security of the assumption is given by showing resilience to linear tests. As a bonus, all of our PRF constructions are key-homomorphic, an algebraic property that is useful in many symmetric-cryptography applications. No previously-known LPN-based PRFs have this property, even if we completely ignore depth-efficiency. In fact, our constructions support key homomorphism for linear functions (and not only additive), a property that no previously-known PRF satisfies, including ones from LWE. Additionally, all of our PRF constructions nicely fit into the substitution-permutation network (SPN) design framework used in modern block ciphers (e.g. AES). No prior PRF construction that has a reduction to a standard cryptographic assumptions (let alone LPN) has an SPN-like structure. Technically, all of our constructions of PFRs leverage a new recursive derandomization technique for LPN instances, which allows us to generate LPN error terms deterministically. This technique is inspired by a related idea from the LWE literature (Kim, EUROCRYPT 2020) for which devising an LPN analogue has been an outstanding open problem.

Original languageEnglish
Title of host publicationSTOC 2025 - Proceedings of the 57th Annual ACM Symposium on Theory of Computing
EditorsMichal Koucky, Nikhil Bansal
PublisherAssociation for Computing Machinery
Pages1898-1909
Number of pages12
ISBN (Electronic)9798400715105
DOIs
StatePublished - 15 Jun 2025
Event57th Annual ACM Symposium on Theory of Computing, STOC 2025 - Prague, Czech Republic
Duration: 23 Jun 202527 Jun 2025

Publication series

NameProceedings of the Annual ACM Symposium on Theory of Computing
ISSN (Print)0737-8017

Conference

Conference57th Annual ACM Symposium on Theory of Computing, STOC 2025
Country/TerritoryCzech Republic
CityPrague
Period23/06/2527/06/25

Bibliographical note

Publisher Copyright:
© 2025 Owner/Author.

Keywords

  • Learning Parity with Noise
  • Pseudorandom Functions

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