通过平滑代码减少解码到 LPN 的局限性

Madhura Pathegama, Alexander Barg
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引用次数: 0

摘要

有噪声学习奇偶校验(LPN)问题强调了几种经典的加密原语。早期的研究使用代码平滑作为实现这种还原的技术工具,表明它们对速率消失的代码是可能的。针对这种情况,我们从解码问题和 LPN 问题的参数出发,描述了减码的效率。作为结论,我们区分了有可能实现有意义缩减的参数区域和不可能实现缩减的参数区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Limitations of the decoding-to-LPN reduction via code smoothing
The Learning Parity with Noise (LPN) problem underlines several classic cryptographic primitives. Researchers have endeavored to demonstrate the algorithmic difficulty of this problem by attempting to find a reduction from the decoding problem of linear codes, for which several hardness results exist. Earlier studies used code smoothing as a technical tool to achieve such reductions, showing that they are possible for codes with vanishing rate. This has left open the question of attaining a reduction with positive-rate codes. Addressing this case, we characterize the efficiency of the reduction in terms of the parameters of the decoding and LPN problems. As a conclusion, we isolate the parameter regimes for which a meaningful reduction is possible and the regimes for which its existence is unlikely.
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