A Symmetric Keyring Encryption Scheme for Biometric Cryptosystems

TL;DR

This paper introduces a new biometric cryptosystem called symmetric keyring encryption (SKE) that securely binds biometric data to cryptographic secrets using a fuzzy symmetric encryption approach, demonstrated with fingerprint data.

Contribution

The paper presents a novel biometric cryptosystem based on symmetric keyring encryption that reframes secret-binding as a fuzzy symmetric encryption problem, differing from traditional primitives.

Findings

SKE can reliably retrieve secrets for genuine biometric inputs.

The scheme maintains security against major attacks.

Empirical results show high accuracy on fingerprint datasets.

Abstract

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.