Biometric Cryptosystems for secret binding such as fuzzy vault and fuzzy commitment are provable secure and offers a convenient way for secret management and protection. Despite numerous practical schemes have been reported, they are deficient in resisting several security and privacy attacks. In this paper, we propose a novel bio-cryptosystem that based on the three key ingredients namely Index of Maximum (IoM) hashing, $(m, k)$(m,k) threshold secret sharing and b-band mini vaults notion. The IoM hashing is motivated from the ranking based Locality Sensitive Hashing theory meant for non-invertible transformation. On the other hand, the $(m, k)$(m,k) threshold secret sharing scheme and the b-band mini vaults manage overcome inherent limitations of biometric cryptosystems when integrated with IoM hashing. The proposed scheme strikes the balance between performance and the privacy/security protection. Unlike fuzzy vault and fuzzy commitment, which primarily devised for unordered and binary biometrics, respectively, our scheme is tailored for feature vector-based biometrics (vectorial biometrics). Comprehensive experiments on fingerprint vectors that derived from several FVC fingerprint benchmarks and rigorous analysis demonstrate decent secret retrieval performance yet offer strong resilience against six major security and privacy attacks.

中文翻译：

---

支持安全秘密共享的 b 波段 Mini Vaults Bio-Cryptosystem for Vectorial Biometrics

用于秘密绑定的生物特征密码系统，例如模糊保险库和模糊承诺，是可证明安全的，并为秘密管理和保护提供了方便的方法。尽管已经报道了许多实用的方案，但它们在抵抗几种安全和隐私攻击方面存在缺陷。在本文中，我们提出了一种新的生物密码系统，它基于三个关键要素，即最大索引（IoM）哈希，$(m, k)$(米,克) 阈值秘密共享和 乙-带迷你金库的概念。IoM 散列的动机来自基于排名的局部敏感散列理论，用于不可逆变换。另一方面，该$(m, k)$(米,克) 阈值秘密共享方案和 乙当与 IoM 散列集成时，-band 迷你金库克服了生物特征密码系统的固有限制。所提出的方案在性能和隐私/安全保护之间取得了平衡。与分别主要为无序和二元生物特征设计的模糊保险库和模糊承诺不同，我们的方案是为基于特征向量的生物特征（矢量生物特征）量身定制的。从几个 FVC 指纹基准和严格分析得出的指纹向量的综合实验证明了不错的秘密检索性能，但对六种主要的安全和隐私攻击提供了强大的弹性。