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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
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Submission history
From: Kel Zin Tan [view email]
[v1] Thu, 4 Jun 2026 08:11:23 UTC (6,933 KB)
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