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 language | English |
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| Title of host publication | STOC 2025 - Proceedings of the 57th Annual ACM Symposium on Theory of Computing |
| Editors | Michal Koucky, Nikhil Bansal |
| Publisher | Association for Computing Machinery |
| Pages | 1898-1909 |
| Number of pages | 12 |
| ISBN (Electronic) | 9798400715105 |
| DOIs | |
| State | Published - 15 Jun 2025 |
| Event | 57th Annual ACM Symposium on Theory of Computing, STOC 2025 - Prague, Czech Republic Duration: 23 Jun 2025 → 27 Jun 2025 |
Publication series
| Name | Proceedings of the Annual ACM Symposium on Theory of Computing |
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| ISSN (Print) | 0737-8017 |
Conference
| Conference | 57th Annual ACM Symposium on Theory of Computing, STOC 2025 |
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| Country/Territory | Czech Republic |
| City | Prague |
| Period | 23/06/25 → 27/06/25 |
Bibliographical note
Publisher Copyright:© 2025 Owner/Author.
Keywords
- Learning Parity with Noise
- Pseudorandom Functions