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31 lip 2015 · In this paper we provide a comprehensive analysis of the existing L P N solving algorithms, both for the general case and for the sparse secret scenario. In practice, the L P N-based cryptographic constructions use as a reference the security parameters proposed by Levieil and Fouque.
6 paź 2022 · The Learning Parity with Noise (LPN) problem is a famous cryptographic problem consisting in recovering a secret from noised samples. This problem is usually solved via reduction techniques, that is, one reduces the original instance to a smaller one before substituting back the recovered unknowns and starting the process again.
31 lip 2015 · In particular, we can now solve instances suggested for 80-bit security in cryptographic schemes like HB variants, LPN-C and Lapin, in less than 280 operations.
1 sty 2015 · A standard conjecture is that the best possible w-error-decoding attacks against random linear codes of dimension k and length n take time 2(α(R,W) + o(1))n if k/n → R and w/n →...
1 lip 2016 · The new theoretical analysis in this paper provides tighter bounds on the complexity of L P N solving algorithms and narrows this gap between theory and practice. We show that for a sparse secret there is another algorithm that outperforms B K W and its variants.
4 lis 2021 · In this work we consider LPN with sparse parities (i.e. the “sparsity” or Hamming weight k of the secret vector is significantly less than \ (\eta \cdot n\), where \ (\eta \) is the error rate). We consider both the constant noise and the low noise setting (where the error rate is subconstant).