Research Article
Privacy-Preserving Preselection for Face Identification Based on Packing
@INPROCEEDINGS{10.1007/978-3-032-23453-7_22, author={Rundong Xin and Taotao Wang and Jin Wang and Chonghe Zhao and Jing Wang}, title={Privacy-Preserving Preselection for Face Identification Based on Packing}, proceedings={Security and Privacy in Communication Networks. 21st EAI International Conference, SecureComm 2025, Xiangtan, China, July 4--6, 2025, Proceedings, Part III}, proceedings_a={SECURECOMM}, year={2026}, month={11}, keywords={Homomorphic encryption Face identification Privacy-preserving Biometric information protection}, doi={10.1007/978-3-032-23453-7_22} }- Rundong Xin
Taotao Wang
Jin Wang
Chonghe Zhao
Jing Wang
Year: 2026
Privacy-Preserving Preselection for Face Identification Based on Packing
SECURECOMM
Springer
DOI: 10.1007/978-3-032-23453-7_22
Abstract
Face identification systems operating in the ciphertext domain have garnered significant attention due to increasing privacy concerns and the potential recovery of original facial data. However, as the size of ciphertext template libraries grows, the face retrieval process becomes progressively more time-intensive. To address this challenge, we propose a novel and efficient scheme for face retrieval in the ciphertext domain, termed Privacy-Preserving Preselection for Face Identification Based on Packing (PFIP). PFIP incorporates an innovative preselection mechanism to reduce computational overhead and a packing module to enhance the flexibility of biometric systems during the enrollment stage. Extensive experiments conducted on the LFW and CASIA datasets demonstrate that PFIP preserves the accuracy of the original face recognition model, achieving a 100% hit rate while retrieving 1,000 ciphertext face templates within 300 ms. Compared to existing approaches, PFIP achieves a nearly 50x improvement in retrieval efficiency.