Sample-Preserving Search-to-Decision Reduction for Noisy Linear Equations over Large Moduli

Reported by arXiv cs.CR (Andrej Bogdanov, Kel Zin Tan, Prashant Nalini Vasudevan)

Summary by Cyber Security News12 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.12913

arXiv:2610.12913v1 Announce Type: new Abstract: The Noisy Linear Equations problem involves finding the solution to a random linear system over a finite field given noisy evaluations. This is a generalisation of various learning problems widely used in cryptography, including Learning with Errors (LWE), Learning with Rounding (LWR), and Learning Parity with Noise (LPN).

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