Generic Constructions of Robustly Reusable Fuzzy Extractor

Wen, Yunhua; Liu, Shengli; Gu, Dawu

doi:10.1007/978-3-030-17259-6_12

Cited by 13 publications

(2 citation statements)

References 26 publications

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“…Also, Wen's scheme is based on a secure sketch. Later, Wen et al [19] proposed two frameworks for developing robust and reusable fuzzy extractors and presented two instantiations in 2019, one of which is an LWE-based one that supports a sub-linear fraction of errors. The second is a DDH-based one that does not support linear fraction of errors and is not quantum resistant.…”

Section: Robust and Reusable Fuzzy Extractormentioning

confidence: 99%

Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with燛rrors Problem

Woo¹,

Kim²,

Park³

2023

Computers, Materials &Amp; Continua

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A fuzzy extractor can extract an almost uniform random string from a noisy source with enough entropy such as biometric data. To reproduce an identical key from repeated readings of biometric data, the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading. In 2013, Fuller et al. proposed a computational fuzzy extractor based on the learning with errors problem. Their construction, however, can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm, which causes the reproducing time to increase significantly. In 2016, Canetti et al. proposed a fuzzy extractor with inputs from low-entropy distributions based on a strong primitive, which is called digital locker. However, their construction necessitates an excessive amount of storage space for the helper data, which is stored in authentication server. Based on these observations, we propose a new efficient computational fuzzy extractor with small size of helper data. Our scheme supports reusability and robustness, which are security notions that must be satisfied in order to use a fuzzy extractor as a secure authentication method in real life. Also, it conceals no information about the biometric data and thanks to the new decoding algorithm can tolerate linear errors. Based on the non-uniform learning with errors problem, we present a formal security proof for the proposed fuzzy extractor. Furthermore, we analyze the performance of our fuzzy extractor scheme and provide parameter sets that meet the security requirements. As a result of our implementation and analysis, we show that our scheme outperforms previous fuzzy extractor schemes in terms of the efficiency of the generation and reproduction algorithms, as well as the size of helper data.

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Section: Robust and Reusable Fuzzy Extractormentioning

confidence: 99%

Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with燛rrors Problem

Woo¹,

Kim²,

Park³

2023

Computers, Materials &Amp; Continua

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“…As a positive result, they directly constructed a lossless fuzzy extractor based on the hardness of learning with errors (LWE) problem. The computational security of fuzzy extractors has been studied in subsequent work [6]- [12].…”

Section: Introductionmentioning

confidence: 99%

On the Limitations of Computational Fuzzy Extractors

Yasunaga

Yuzawa

2023

IEICE Trans. Fundamentals

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We present a negative result of fuzzy extractors with computational security. Specifically, we show that, under a computational condition, a computational fuzzy extractor implies the existence of an information-theoretic fuzzy extractor with slightly weaker parameters. Our result implies that to circumvent the limitations of information-theoretic fuzzy extractors, we need to employ computational fuzzy extractors that are not invertible by non-lossy functions.

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