Abstract

A fuzzy extractor derives uniformly random strings from noisy sources that are neither reliably reproducible nor uniformly random. The basic definition of fuzzy extractor was first formally introduced by Dodis et al. and has achieved various applications in cryptographic systems. However, it has been proved that a fuzzy extractor could become totally insecure when the same noisy random source is extracted multiple times. To solve this problem, the reusable fuzzy extractor is proposed. In this paper, we propose the first reusable fuzzy extractor based on the LPN assumption, which is efficient and resilient to linear fraction of errors. Furthermore, our construction serves as an alternative post-quantum reusable fuzzy extractor.

中文翻译：

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基于LPN假设的可重用模糊提取器

摘要

模糊提取器从既不能可靠地再现又不能均匀随机的噪声源中获得均匀随机的字符串。Dodis等人首先正式引入了模糊提取器的基本定义。并且已经在密码系统中实现了各种应用。然而，已经证明，当多次提取相同的噪声随机源时，模糊提取器可能变得完全不安全。为了解决这个问题，提出了一种可重用的模糊提取器。在本文中，我们提出了基于LPN假设的第一个可重用的模糊提取器，该提取器高效且对线性误差部分具有弹性。此外，我们的构造还可以用作后量子可重用的模糊提取器。