Face recognition models operate in a client-server setting where a client extracts a compact face embedding and a server performs similarity search over a template database. This raises privacy concerns, as facial data is highly sensitive. To provide cryptographic privacy guarantees, one can use fully homomorphic encryption to perform end-to-end encrypted similarity search. However, existing FHE-based protocols are computationally costly and, impose high memory overhead. Building on prior work, HyDia, we introduce algorithmic and system-level improvements targeting real-world deployment with resource-constrained clients. First, we propose BSGS-Diagonal, an algorithm delivering fast and memory-efficient similarity computation. BSGS-Diagonal substantially shrinks the rotation-key set, lowering both client and server memory requirements, and also improves practical server runtime. This yields a 91% reduction in the number of rotation keys, translating to approximately 14 GB less memory used on the client, and reducing overall CPU peak RAM from over 30 GB in the original HyDia to under 10 GB for databases up to size 1M. In addition, runtime is improved by up to 1.57x for the membership verification scenario and 1.43x for the identification scenario. Secondly, we introduce fully GPU-optimized similarity matrix computation kernels. The implementation is built upon FIDESlib, a CKKS-level GPU library based on OpenFHE. Rather than offloading individual CKKS primitives in isolation, the integrated kernels fuse operations to avoid repeated CPU-GPU ciphertext movement and costly FIDESlib/OpenFHE data-structure conversions. As a result, our GPU implementations of both HyDia and BSGS-Diagonal achieve up to 9x and 17x speedups, respectively, enabling sub-second encrypted face recognition for databases up to 32K entries while further reducing host memory usage.

翻译：人脸识别模型通常采用客户端-服务器架构运行：客户端提取紧凑的人脸嵌入向量，服务器在模板数据库上进行相似性搜索。由于面部数据具有高度敏感性，这引发了隐私问题。为提供密码学层面的隐私保障，可采用全同态加密实现端到端的加密相似性搜索。然而，现有基于FHE的协议计算成本高昂且内存开销巨大。我们在前人工作HyDia的基础上，针对资源受限客户端的实际部署场景，引入了算法与系统层面的改进。首先，我们提出BSGS-Diagonal算法，该算法能实现快速且内存高效的相似性计算。BSGS-Diagonal大幅缩减了旋转密钥集，同时降低了客户端与服务端的内存需求，并提升了实际场景中的服务端运行效率。旋转密钥数量减少91%，相当于客户端内存占用减少约14 GB，且对于容量达100万的数据库，整体CPU峰值内存从原始HyDia的30 GB以上降至10 GB以下。此外，成员验证场景的运行效率提升高达1.57倍，识别场景提升达1.43倍。其次，我们引入了完全GPU优化的相似性矩阵计算内核。该实现基于FIDESlib（一款基于OpenFHE的CKKS层级GPU库）。我们不单独卸载单个CKKS原语，而是通过融合操作来集成内核，避免了重复的CPU-GPU密文传输及昂贵的FIDESlib/OpenFHE数据结构转换。实验结果表明，基于GPU实现的HyDia与BSGS-Diagonal分别实现了高达9倍和17倍的加速比，从而在支持32K条目的数据库上实现了亚秒级加密人脸识别，同时进一步降低了主机内存占用。