Fuzzy extractor (FE) extracts and reproduces a uniform string from a fuzzy source. Robustly reusable fuzzy extractor (rrFE) considers reusability and robustness simultaneously. Reusability of rrFE allows multiple extractions of pseudorandom strings from the same source and robustness detects active attacks. To achieve reusability and robustness, the existing constructions of rrFE make heavy use of perfect random coins (which are uniformly distributed and independent of each other), besides the fuzzy source. However, efficiently sampling unbiased random bits only exists in the ideal world. In this paper, we show how to construct rrFE resorting to imperfect randomness (non-uniform but of high entropy), which is easy to sample in practice. We propose two generic constructions of rrFE in the CRS model, with one construction dealing with perfect randomness and the other dealing with imperfect randomness. We also present two instantiations of rrFE from the DDH and LPN assumptions working with perfect randomness, and another two instantiations of rrFE from DDH and LPN working with imperfect randomness. All instantiations support linear fraction of errors between samples of the fuzzy source.

• Our DDH-based rrFE (both rrFE with perfect randomness and rrFE with imperfect randomness) are the first tightly secure rrFEs in the standard model, i.e., the reusability and robustness are tightly reduced to the DDH assumption. Compared with the DDH-based rrFE scheme in PKC2019 by Wen et al., our rrFE enjoys tighter security, better efficiency, and support of usage of imperfect randomness.

• Our LPN-based rrFE (both rrFE with perfect randomness and rrFE with imperfect randomness) are the first rrFEs from the LPN assumption in the standard model.

模糊提取器（FE）从模糊源提取并再现统一的字符串。健壮的可重用模糊提取器（rrFE）同时考虑了可重用性和鲁棒性。rrFE的可重用性允许从同一来源多次提取伪随机字符串，而健壮性可检测到主动攻击。为了获得可重用性和鲁棒性，除了模糊源之外，现有的rrFE构造还大量使用完美的随机硬币（均匀分布且彼此独立）。然而，仅在理想世界中有效地采样无偏随机比特。在本文中，我们展示了如何构造不完善的随机性（非均匀但具有高熵）的rrFE，这在实践中很容易采样。我们在CRS模型中提出了rrFE的两种通用构造，一种结构处理完全随机性，另一种结构处理不完全随机性。我们还提出了从DDH和LPN假设以完美随机性对rrFE的两个实例化，以及从DDH和LPN假设以非完美随机性进行的rrFE的另外两个实例化。所有实例都支持模糊源样本之间误差的线性比例。

• 我们基于DDH的rrFE（具有完美随机性的rrFE和具有不完善随机性的rrFE）是标准模型中的第一个紧密安全的rrFE，即，将可重用性和鲁棒性严格降低到DDH假设。与Wen等人在PKC2019中基于DDH的rrFE方案相比，我们的rrFE具有更严格的安全性，更高的效率以及对不完美随机性的支持。

• 我们基于LPN的rrFE（具有完美随机性的rrFE和具有不完善随机性的rrFE）都是标准模型中基于LPN假设的首批rrFE。