Abstract
Fuzzy extractor converts the reading of a noisy non-uniform source to a reproducible and almost uniform output \(\mathsf {R}\). The output \(\mathsf {R}\) in turn is used in some cryptographic system as a secret key. To enable multiple extractions of keys \(\mathsf {R}_1, \mathsf {R}_2, \ldots , \mathsf {R}_\rho \) from the same noisy non-uniform source and applications of different \(\mathsf {R}_i\), the concept of reusable fuzzy extractor is proposed to guarantee the pseudorandomness of \(\mathsf {R}_i\) even conditioned on other extracted keys \(\mathsf {R}_j\) (from the same source).
In this work, we construct a reusable fuzzy extractor from the Learning With Errors (LWE) assumption. Our reusable fuzzy extractor provides resilience to linear fraction of errors. Moreover, our construction is simple and efficient and imposes no special requirement on the statistical structure of the multiple readings of the source.
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This work was supported by the National Natural Science Foundation of China (NSFC No. 61672346).
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Wen, Y., Liu, S. (2018). Reusable Fuzzy Extractor from LWE. In: Susilo, W., Yang, G. (eds) Information Security and Privacy. ACISP 2018. Lecture Notes in Computer Science(), vol 10946. Springer, Cham. https://doi.org/10.1007/978-3-319-93638-3_2
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