Fuzzy extractors: How to generate strong keys from biometrics and other noisy data
Fuzzy extractors: How to generate strong keys from biometrics and other noisy data
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DOI：
10.1137/060651380
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发表时间：
2008-01-01
影响因子：
1.6
通讯作者：
Smith, Adam
中科院分区：
文献类型：
--
作者：
Dodis, Yevgeniy;Ostrovsky, Rafail;Smith, Adam
我们提供了正式的定义和有效的安全技术，将嘈杂的信息转化为可用于任何加密应用程序的密钥，特别是可靠和安全地验证生物特征数据。我们的技术不仅适用于生物特征信息，而且适用于任何密钥材料，与传统的加密密钥不同，（1）不可精确再现，（2）不均匀分布。我们提出两个原语：模糊提取器从其输入中可靠地提取几乎均匀的随机性R;该提取在即使输入改变R也将相同的意义上是容错的，只要它保持合理地接近原始。因此，R可以用作加密应用中的密钥。一个安全的草图会产生关于其输入w的公共信息，该信息不会泄露w，但允许在给定另一个接近w的值的情况下精确恢复w。因此，它可以用来可靠地再现容易出错的生物特征输入，而不会引发存储它们时固有的安全风险。我们去。新的原语是正式的安全和通用的，概括了许多以前的工作。此外，我们提供了几乎最佳的结构，这两个原语的各种措施的“接近”的输入数据，如汉明距离，编辑距离，并设置差异。
We provide formal definitions and efficient secure techniques for turning noisy information into keys usable for any cryptographic application, and, in particular, reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a fuzzy extractor reliably extracts nearly uniform randomness R from its input; the extraction is error-tolerant in the sense that R will be the same even if the input changes, as long as it remains reasonably close to the original. Thus, R can be used as a key in a cryptographic application. A secure sketch produces public information about its input w that does not reveal w and yet allows exact recovery of w given another value that is close to w. Thus, it can be used to reliably reproduce error-prone biometric inputs without incurring the security risk inherent in storing them. We de. ne the primitives to be both formally secure and versatile, generalizing much prior work. In addition, we provide nearly optimal constructions of both primitives for various measures of "closeness" of input data, such as Hamming distance, edit distance, and set difference.