Among biometric verification systems, irises stand out because they offer high accuracy even in large-scale databases. For example, the World ID project aims to provide authentication to all humans via iris recognition, with millions already registered. Storing such biometric data raises privacy concerns, which can be addressed using privacy-enhancing techniques. Bloemen et al. describe a solution based on 2-out-of-3 Secret-Sharing Multiparty Computation (SS-MPC), for the World ID setup. In terms of security, unless an adversary corrupts 2~servers, the iris codes remain confidential and nothing leaks beyond the result of the computation. Their solution is able to match~$32$ users against a database of~$2^{22}$ iris codes in~$\approx 2$s , using~24 H100 GPUs, more than 40~communication rounds and $81$GB/party of data transferred (the timing assumes a network speed above~3Tb/s). In the present work, we explore the use of Threshold Fully Homomorphic Encryption (ThFHE) for the same task. The ThFHE solution brings a number of security advantages: no trusted setup, the encrypted database and queries can be public, the secret can be distributed among many parties, and active security can be added without significant performance degradation. Our proof-of-concept implementation of the computation phase handles $32$~eyes against a database of $7\cdot 2^{14}$ iris codes in~$\approx 1.8$s ($\approx 0.33s$ for 4 eyes against the same database), using 8 RTX-5090 GPUs. To this, one should add~2 to 3 rounds of communication (depending on deployment choice). We perform the matching using the CKKS (Th)FHE scheme. Our main technical ingredients are the use of recent progress on FHE-based linear algebra boosted using int8 GPU operations, and the introduction of a technique reducing the number of ciphertexts to be processed as early as possible.

翻译：在生物特征验证系统中，虹膜识别因其即使在大规模数据库中也能提供高精度而脱颖而出。例如，World ID 项目旨在通过虹膜识别为所有人提供身份认证，目前已注册数百万用户。存储此类生物特征数据引发了隐私担忧，这可以通过隐私增强技术来解决。Bloemen 等人针对 World ID 的设置，描述了一种基于 2-out-of-3 秘密共享多方计算（SS-MPC）的解决方案。在安全性方面，除非攻击者攻破 2 台服务器，否则虹膜码将保持机密，除了计算结果外不会泄露任何信息。他们的解决方案能够在大约 2 秒内，使用 24 块 H100 GPU、超过 40 轮通信以及每方 81GB 的数据传输量（该时间假设网络速度高于 3Tb/s），将 32 个用户与一个包含 2^{22} 个虹膜码的数据库进行匹配。在本工作中，我们探索了使用门限全同态加密（ThFHE）来完成同一任务。ThFHE 解决方案带来了多项安全优势：无需可信设置、加密数据库和查询可以公开、密钥可以分发给多方，并且可以在不显著降低性能的情况下增加主动安全性。我们的概念验证计算阶段实现，使用 8 块 RTX-5090 GPU，在大约 1.8 秒内（对于相同数据库中的 4 只眼睛，大约为 0.33 秒）处理 32 只眼睛与一个包含 7·2^{14} 个虹膜码的数据库的匹配。此外，还需要增加 2 到 3 轮通信（取决于部署选择）。我们使用 CKKS (Th)FHE 方案执行匹配。我们的主要技术要素包括：利用基于 FHE 的线性代数的最新进展，并通过 int8 GPU 操作进行加速；以及引入一种尽早减少待处理密文数量的技术。