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arXiv:2611.12636 · v1

BioBind: Binding Biometric Deduplication to Zero-Knowledge Authentication

首次提交 2026-11-10 00:13
版本更新 2026-11-10 00:13
雷达观测 2026-11-10 15:22
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摘要

Biometric identity systems for decentralized applications need credentials that resist repeated enrollment and support private, publicly verifiable authentication. Capture and large-scale private deduplication run off chain while smart contracts authorize issuance and use; these stages span distinct trust domains, and a correct verdict covers only the checked inputs and gallery snapshot. Without binding and state checks, concurrent enrollments can issue duplicate credentials from correct verdicts, while substituted inputs can authorize unrelated credentials. We present BioBind, a protocol whose four backend interface conditions bind the deduplication query, gallery insertion and issued commitment to one captured vector, authenticate gallery transitions, and require a sound uniqueness verdict valid at finalization. Its encrypted instance uses bound requests, stale-session rejection and committee-confirmed migration. Capture-bound zero-knowledge authentication links credential use to the enrolled template and a fresh device-originated probe bound to the challenge. We give a conditional multi-session composition argument under explicit capture, backend and cryptographic assumptions. A prototype using LFW face features rejects the tested composition attacks; its unlinkable authentication circuit uses 31,445 constraints and about 305,000 gas. An equal-public-field baseline quantifies the cost of verifiable matching: witness-plus-proving medians are 1.989 s versus 1.119 s with device-trusted matching. Separate tests of encrypted enrollment complete three histories of 16,384 registrations without decoding errors at the tested checkpoints. Biometric accuracy bounds person-level uniqueness: on the Bosphorus database, cooperative subjects who change appearance evade deduplication in 1.8% of re-enrollment attempts at the operating threshold. Full backend integration remains open.

作者备注:29 pages, 7 figures, 19 tables. Supplementary material included as Appendices A-H. Code and artifact: https://github.com/plan-lab-szu/biobind

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v1 · 2026-11-10 00:13 · 最新观测版本

BioBind: Binding Biometric Deduplication to Zero-Knowledge Authentication

Biometric identity systems for decentralized applications need credentials that resist repeated enrollment and support private, publicly verifiable authentication. Capture and large-scale private deduplication run off chain while smart contracts authorize issuance and use; these stages span distinct trust domains, and a correct verdict covers only the checked inputs and gallery snapshot. Without binding and state checks, concurrent enrollments can issue duplicate credentials from correct verdicts, while substituted inputs can authorize unrelated credentials. We present BioBind, a protocol whose four backend interface conditions bind the deduplication query, gallery insertion and issued commitment to one captured vector, authenticate gallery transitions, and require a sound uniqueness verdict valid at finalization. Its encrypted instance uses bound requests, stale-session rejection and committee-confirmed migration. Capture-bound zero-knowledge authentication links credential use to the enrolled template and a fresh device-originated probe bound to the challenge. We give a conditional multi-session composition argument under explicit capture, backend and cryptographic assumptions. A prototype using LFW face features rejects the tested composition attacks; its unlinkable authentication circuit uses 31,445 constraints and about 305,000 gas. An equal-public-field baseline quantifies the cost of verifiable matching: witness-plus-proving medians are 1.989 s versus 1.119 s with device-trusted matching. Separate tests of encrypted enrollment complete three histories of 16,384 registrations without decoding errors at the tested checkpoints. Biometric accuracy bounds person-level uniqueness: on the Bosphorus database, cooperative subjects who change appearance evade deduplication in 1.8% of re-enrollment attempts at the operating threshold. Full backend integration remains open.

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