The paper introduces a new cryptographic primitive called a *fuzzy commitment scheme*, which combines techniques from error-correcting codes and cryptography. This scheme is designed to be both *concealing* and *binding*, meaning it is infeasible for an attacker to learn the committed value and for the committer to decommit a value in more than one way. Unlike conventional commitment schemes, the fuzzy commitment scheme is *fuzzy*, allowing a witness that is close to the original encrypting witness to be used for decommitment, even if not identical. The authors highlight the utility of this scheme in biometric authentication systems, where data is subject to random noise. The scheme's ability to tolerate errors makes it suitable for protecting biometric data, addressing a significant challenge in biometric authentication. The security of the fuzzy commitment scheme is proven relative to the properties of an underlying cryptographic hash function. The paper also discusses the organization of the paper, background on biometrics and related work, and the construction of the fuzzy commitment scheme. It includes a detailed description of how the scheme works, its applications in static authentication, challenge-response authentication, and encryption/decryption, and an analysis of its security and resilience. The authors conclude by discussing future research directions, including the distribution of inputs in real-world applications and the types of error patterns common in biometric systems.The paper introduces a new cryptographic primitive called a *fuzzy commitment scheme*, which combines techniques from error-correcting codes and cryptography. This scheme is designed to be both *concealing* and *binding*, meaning it is infeasible for an attacker to learn the committed value and for the committer to decommit a value in more than one way. Unlike conventional commitment schemes, the fuzzy commitment scheme is *fuzzy*, allowing a witness that is close to the original encrypting witness to be used for decommitment, even if not identical. The authors highlight the utility of this scheme in biometric authentication systems, where data is subject to random noise. The scheme's ability to tolerate errors makes it suitable for protecting biometric data, addressing a significant challenge in biometric authentication. The security of the fuzzy commitment scheme is proven relative to the properties of an underlying cryptographic hash function. The paper also discusses the organization of the paper, background on biometrics and related work, and the construction of the fuzzy commitment scheme. It includes a detailed description of how the scheme works, its applications in static authentication, challenge-response authentication, and encryption/decryption, and an analysis of its security and resilience. The authors conclude by discussing future research directions, including the distribution of inputs in real-world applications and the types of error patterns common in biometric systems.