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Towards Worst-case Hardness for Low-Noise LPN

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arXiv:2606.05834v1 Announce Type: new Abstract: The hardness of the Learning Parity with Noise (LPN) problem is a foundational assumption in cryptography, forming the basis of constructions ranging from symmetric-key primitives to public-key encryption and beyond. A central open question is whether the average-case hardness of LPN can be based on worst-case complexity assumptions, as has been achieved for the analogous Learning With Errors (LWE) problem. Existing worst-case-to-average-case reduc

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    Computer Science > Cryptography and Security [Submitted on 4 Jun 2026] Towards Worst-case Hardness for Low-Noise LPN Divesh Aggarwal, Rishav Gupta, Hai Hoang Nguyen, Kel Zin Tan, Prashant Nalini Vasudevan The hardness of the Learning Parity with Noise (LPN) problem is a foundational assumption in cryptography, forming the basis of constructions ranging from symmetric-key primitives to public-key encryption and beyond. A central open question is whether the average-case hardness of LPN can be based on worst-case complexity assumptions, as has been achieved for the analogous Learning With Errors (LWE) problem. Existing worst-case-to-average-case reductions for LPN [BLVW19, YZ21] rely on statistical smoothing of linear codes, which inherently limits the resulting average-case hardness to noise rates as large as 1/2 - 1/\mathrm{poly}(n), which is insufficient for public-key applications. We explore a new approach towards obtaining such reductions: rather than requiring that random sparse combinations of the rows of the generator matrix of a code be statistically close to uniform, we only require that they be computationally indistinguishable from uniform. This leads to a clean win-win structure: we show that any efficient LPN solver can be transformed into a pair of efficient algorithms (S, D) such that for every matrix A of appropriate dimensions over \mathbb{F}_2, either S decodes the code generated by A from random noise, or D distinguishes random noisy codewords of the dual of this code from uniform. By instantiating this reduction with appropriate parameters, we obtain the average-case hardness of LPN with inverse-polynomial noise rate n^{-\alpha} for any constant \alpha < 1, assuming the worst-case simultaneous hardness of decoding a code from random noise and distinguishing random noisy codewords of its dual from uniform. In particular, setting \alpha = 1/2, our reduction yields LPN hardness in the parameter regime required for Alekhnovich's construction of public-key encryption [Ale03], a regime that was previously inaccessible via worst-case reductions. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.05834 [cs.CR]   (or arXiv:2606.05834v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.05834 Focus to learn more Submission history From: Kel Zin Tan [view email] [v1] Thu, 4 Jun 2026 08:11:23 UTC (6,933 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Jun 05, 2026
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    Jun 05, 2026
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