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A New Algebraic Approach to the Regular Syndrome Decoding Problem and Implications for PCG Constructions

Authors: Pierre Briaud; Morten Øygarden;

A New Algebraic Approach to the Regular Syndrome Decoding Problem and Implications for PCG Constructions

Abstract

The Regular Syndrome Decoding (RSD) problem, a variant of the Syndrome Decoding problem with a particular error distribution, was introduced almost 20 years ago by Augot et al.. In this problem, the error vector is divided into equally sized blocks, each containing a single noisy coordinate. More recently, the last five years have seen increased interest in this assumption due to its use in MPC and ZK applications. Generally referred to as "LPN with regular noise" in this context, the assumption allows to achieve better efficiency when compared to plain LPN. In all previous works of cryptanalysis, it has not been shown how to exploit the special feature of this problem in an attack. We present the first algebraic attack on RSD. Based on a careful theoretical analysis of the underlying polynomial system, we propose concrete attacks that are able to take advantage of the regular noise distribution. In particular, we can identify several examples of concrete parameters where our techniques outperform other algorithms.

Keywords

Pseudorandom Correlation Generator, Learning Parity with Noise, Algebraic cryptanalysis, Regular distribution, [INFO] Computer Science [cs]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Top 10%
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