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Fuzzy extractors convert biometric data into random strings, which makes it possible to apply cryptographic techniques for biometric security. They are used to encrypt and authenticate users records, with biometric inputs as a key. Historically, the first biometric system of this kind was designed by Juels and Wattenberg and was called "Fuzzy commitment", where the cryptographic key is decommitted using biometric data. "Fuzzy", in that context, implies that the value close to the original one can extract the committed value. Later, Juels and Sudan came up with (Fuzzy vault ) schemes which are order invariant for the fuzzy commitment scheme but uses a Reed–Solomon code. Codeword is evaluated by polynomial and the secret message is inserted as the coefficients of the polynomial. The polynomial is evaluated for different values of a set of features of the biometric data. So Fuzzy commitment and Fuzzy Vault were per-cursor to Fuzzy extractors. Fuzzy extractor is a biometric tool to authenticate a user using its own biometric template as a key. They extract uniform and random string from its input that has tolerance for noise. If the input changes to but is still close to , the string can still be re-constructed. When is used first time to re-construct, it outputs a helper string which can be made public without compromising the security of (used for encryption and authentication key) and (helper string) is stored to recover . They remain secure even when the adversary modifies (key agreement between a user and a server based only on a biometric input). This article is based on the papers "Fuzzy Extractors: A Brief Survey of Results from 2004 to 2006" and "Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data" by Yevgeniy Dodis, Rafail Ostrovsky, Leonid Reyzin and Adam Smith ==Motivation== As fuzzy extractors deal with how to generate strong keys from Biometrics and other Noisy Data, it applies cryptography paradigms to biometric data and that means (1) Make little assumptions about the biometric data (these data comes from variety of sources and don't want adversary to exploit that so it is best to assume the input is unpredictable) (2) Apply cryptographic application techniques to the input. (for that fuzzy extractor converts biometric data into secret, uniformly random and reliably reproducible random string). According to "Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data" paper by Yevgeniy Dodis, Rafail Ostrovsky, Leonid Reyzin and Adam Smith – these techniques also have other broader applications (when noisy inputs are used) such as human memory, images used as passwords, keys from quantum channel. Based on the Differential Privacy paper by Cynthia Dwork (ICALP 2006) – fuzzy extractors have application in the proof of impossibility of strong notions of privacy for statistical databases. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Fuzzy extractor」の詳細全文を読む スポンサード リンク
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