Paper 2026/1930

Sample-then-Correct: Practical fuzzy extractors for biometrics beyond the iris

Abstract

Fuzzy extractors derive stable cryptographic keys from noisy biometric measurements (Dodis et al., EUROCRYPT 2004). Despite nearly three decades of research, existing fuzzy extractor constructions do not provide strong security for the biometric modalities deployed on modern devices, particularly fingerprints and facial recognition. Recent attacks highlight the gap between known analysis of theoretical constructions and the security levels required for practical deployment (Zhu and Wang, PoPETS 2025). We close this gap. Compared with sample-then-lock baselines (Shukla et al., CCS 2025), our construction improves security for the fingerprint and face biometrics from $44$ to $72$ bits and from $59$ to $84$ bits, respectively. For iris biometric, we improve security from $105$ to $111$ bits. Our construction, sample-then-correct, combines two existing approaches: digital lockers and secure sketches based on error-correcting codes. We embed secure sketches within a collection of digital lockers that an honest party can open with good probability. The construction naturally achieves the benefits of both approaches. We prove security in an extension of the generic group model that measures natural attacks the adversary can conduct. These gains are achieved while maintaining authentication accuracy comparable to prior work, with true accept rates near $90\%$ when multiple readings are available and approximately $60\%$ from a single reading. Our implementation authenticates in under one second on a single CPU thread.