BKW Meets Fourier New Algorithms for LPN with Sparse Parities

Dachman-Soled, Dana; Gong, Huijing; Kippen, Hunter; Shahverdi, Aria

doi:10.1007/978-3-030-90453-1_23

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2022

2023

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Cited by 2 publications

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“…This algorithm employs a new numerical data structure, in contrast to vector quantities, throughout the process [15]. The article [16] studies the Learning Parity with Noise (LPN) problem with a sparse secret key.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Algorithm for LPN Decoding with Improved Memory and Time Efficiency

Yin

Jin

2023

Preprint

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The LPN problem, which forms the basis of many lightweight and post-quantum cryptographic schemes, has recently received considerable attention. The solution to this problem typically involves two distinct phases, namely the reduce phase and the decode phase. While current research primarily focuses on the reduce phase, the most advanced decoding algorithm, FWHT, suffers from high computational costs in terms of time and space complexity. In this article, we propose a novel LPN decoding algorithm, GNS, which utilizes a heuristic approach to achieve significantly reduced memory and time complexity compared to FWHT. Experimental comparisons between the FWHT and GNS algorithms were conducted, with the computational results demonstrating that the proposed GNS algorithm is both efficient and superior to FWHT.

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Section: Introductionmentioning

confidence: 99%