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