Paper 2018/681

A Reusable Fuzzy Extractor with Practical Storage Size

Jung Hee Cheon, Jinhyuck Jeong, Dongwoo Kim, and Jongchan Lee

Abstract

After the concept of a Fuzzy Extractor (FE) was rst introduced by Dodis et al. , it has been regarded as one of the candidate solutions for key management utilizing biometric data. With a noisy input such as biometrics, FE generates a public helper value and a random secret key which is reproducible given another input similar to the original input. However, "helper values" may cause some leakage of information when generated repeatedly by correlated inputs, thus reusability should be considered as an important property. Recently, Canetti et al. (Eurocrypt 2016) proposed a FE satisfying both reusability and robustness with inputs from low-entropy distributions. Their strategy, the so-called Sample-then-Lock method, is to sample many partial strings from a noisy input string and to lock one secret key with each partial string independently. In this paper, modifying this reusable FE, we propose a new FE with size-reduced helper data hiring a threshold scheme. Our new FE also satises both reusability and robustness, and requires much less storage memory than the original. To show the advantages of this scheme, we analyze and compare our scheme with the original in concrete parameters of the biometric, IrisCode. As a result, on 1024-bit inputs, with false rejection rate 0.5 and error tolerance 0.25, while the original requires about 1TB for each helper value, our scheme requires only 300MB with an additional 1.35GB of common data which can be used for all helper values.