Blockchain-based identity services let applications across administrative domains verify biometric authentication results without sharing a trusted biometric server. BioZero evaluates how to protect private templates while limiting on-chain execution and submitted transaction-data costs under explicit cryptographic assumptions. FullCheck provides knowledge soundness for the modular distance relation, but exhaustive coordinate checks cost 22.1 million gas at N=128 in a threshold-stand-in benchmark. SpotCheck tests a natural optimization: sampling checks within the same homomorphic structure. Checking seven coordinates saves 81.3% gas, reducing it to 4.1 million, but retains 127,844 bytes of calldata. We prove that changing one product commitment can shift the reconstructed distance to any chosen residue and evade a uniform sample of t among N coordinates with probability 1-t/N. Negligible attack success therefore requires nearly exhaustive checking, limiting the scope for coordinate-check savings. CircuitCheck changes the verification structure, proving commitment openings, coordinate ranges, and the integer distance threshold in one circuit. Its complete reference transaction costs 255 thousand gas with 356 bytes of calldata. At N=128, median proving time and process peak memory are 0.63 s and 822 MiB on the evaluated workstation. On two face datasets, every tested width of at least 6 bits stays within 0.46 percentage points of float accuracy, supporting the shared integer-input front end. These results favor CircuitCheck for the evaluated integer matching task in blockchain-backed cross-domain identity services, provided that committed probes are separately bound to authenticated live captures.
Large-scale biometric identification in outsourced settings requires two properties simultaneously: biometric templates and queries must remain protected during computation, and the encrypted similarity outputs produced by an untrusted compute node must be verifiably correct before any application result is released. Existing FHE-based biometric systems primarily address confidentiality, while practical verifiability introduces two bottlenecks in the underlying encrypted 1:N matching layer: rotation- and bandwidth-heavy similarity evaluation and the high cost of proving repeated homomorphic similarity traces. We present BioZKFHE, a framework for scalable encrypted biometric identification via verifiable homomorphic similarity evaluation that combines BGV homomorphic computation with committee-based threshold opening, verification, and decryption, and smart-contract enforcement of the committee's verification attestations. To reduce encrypted storage and avoid rotation-heavy encrypted 1:N matching, we propose Single-Coefficient Multi-Value (SCMV) packing, which binds multiple quantized embedding values into each plaintext entry through base-T expansion. To make proof generation practical, we propose Parallelizable and Verifiable Similarity Computation (PVSC), which exploits the Double-CRT execution structure of BGV to decompose each blockwise similarity trace into parallel proof instances that are opened and checked before result release. Under standard lattice assumptions and explicit committee/verifier assumptions, we analyze recoverability, noise growth, confidentiality, encrypted-output integrity, and finalized-result integrity. Experiments on FaceNet and MobileFaceNet show near-lossless biometric utility, up to 67% encrypted-storage reduction, and about 22-44 s end-to-end proof-verified runtime for 10k-40k templates.