  In this paper, we propose a novel biometric cryptosystem for vectorial
biometrics named symmetric keyring encryption (SKE) inspired by Rivest's
keyring model (2016). Unlike conventional biometric secret-binding primitives,
such as fuzzy commitment and fuzzy vault, the proposed scheme reframes the
biometric secret-binding problem as a fuzzy symmetric encryption problem with a
notion called resilient vector pair. In this study, the pair resembles the
encryption-decryption key pair in symmetric key cryptosystems. This notion is
realized using the index of maximum hashed vectors - a special instance of the
ranking-based locality-sensitive hashing function. With a simple filtering
mechanism and [m,k] Shamir's secret-sharing scheme, we show that SKE, both in
theoretical and empirical evaluation, can retrieve the exact secret with
overwhelming probability for a genuine input yet negligible probability for an
imposter input. Though SKE can be applied to any vectorial biometrics, we adopt
the fingerprint vector as a case of study in this work. The experiments have
been performed under several subsets of FVC 2002, 2004, and 2006 datasets. We
formalize and analyze the threat model of SKE that encloses several major
security attacks.
