Fuzzy Extractors are Practical: Cryptographic Strength Key Derivation from the Iris

Despite decades of effort, a persistent chasm has existed between the theory and practice of device-level biometric authentication. Theoretical constructions can, in principle, provide biometric authentication with cryptographically secure public enrollment data. However, concrete implementations of these techniques have failed to provide security with real-world parameters. The result is that deployed authentication algorithms rely on data that overtly leaks private information about the biometric; thus systems rely on externalized security measures such as trusted execution environments. We close this chasm. We introduce a key derivation system with 105 bits of entropy and a 92% true accept rate (TAR) for the iris. Our system advances 1) the feature extraction from the iris and 2) the fuzzy extractor used to derive keys. The fuzzy extractor builds on sample-then-lock (Canetti et al., Journal of Cryptology 2021). We (1) Introduce a new sampling method with a better trade-off between TAR and entropy when features have different quality, (2) Correct Canetti et al. 's main security proof, showing the minimum of min-entropy over subsets is the relevant security measure, and (3) Tighten Canetti et al.'s concrete analysis, nearly doubling security under reasonable assumptions. Our final feature extractor incorporates ideas from the new sampling method to produce features optimized for the sample-thenlock construction.