Algorithms for Sparse LPN and LSPN Against Low-noise
<|reference_start|>Algorithms for Sparse LPN and LSPN Against Low-noise: We study learning algorithms for two sparse variants of the classical learning parity with noise (LPN) problem. We provide a new algorithmic framework that improves the state of the art for a wide range of parameters. This framework has a simple structure different from previous approaches: the first step is a domain reduction via the knowledge of sparsity; then it solves sub-problems by Gaussian elimination. Let $n$ be the dimension, $k$ be the sparsity parameter, and $\eta$ be the noise rate such that each label gets flipped with probability $\eta$.   The sparse LPN problem (with various parameters) has wide applications in cryptography. Different from the standard LPN problem that samples random vectors in $\mathbf{F}_2^n$, it samples random $k$-sparse vectors. The birthday paradox implies a trivial distinguishing algorithm given $m=n^{k/2}$ samples. For $m=n^{1+(\frac{k}{2}-1)(1-\delta)}$ with $\delta \in (0,1)$, the best known algorithm has running time $\min\{e^{\eta n}, e^{\tilde{O}(n^{\delta})}\}$. We present a learning algorithm for sparse LPN with time complexity $e^{\tilde{O}(\eta \cdot n^{\frac{1+\delta}{2}})}$ and sample complexity $m=\max\{1,\frac{\eta \cdot n^{\frac{1+\delta}{2}}}{k^2}\} \cdot \tilde{O}(n)^{1+(\frac{k-1}{2})(1-\delta)}$. It improves previous results for any constant or super-constant $k$ with a wide range of $\eta$.   The learning sparse parity with noise (LSPN) problem assumes the hidden parity is $k$-sparse. LSPN has been extensively studied in both learning theory and cryptography. However, the state of the art needs ${n \choose k/2} = \Omega(n/k)^{k/2}$ time for a wide range of parameters while the simple enumeration algorithm takes ${n \choose k}=O(n/k)^k$ time. Our LSPN algorithm runs in time $O(\eta \cdot n/k)^k$ for any $\eta$ and $k$. This improves the state-of-the-art for learning sparse parity in a wide range of parameters.<|reference_end|>
@article{chen2024algorithms,
    title={Algorithms for Sparse LPN and LSPN Against Low-noise},
    author={Xue Chen, Wenxuan Shu, Zhaienhe Zhou},
    journal={arXiv preprint arXiv:2407.19215},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19215},
    primaryClass={cs.CR}
}