A three-party iris recognition model based on homomorphic encryption and CS-LBP feature extraction algorithm

With the widespread adoption of biometric technologies, data privacy and security have become pressing concerns. This study proposes a three-party iris recognition model based on fully homomorphic encryption (FHE) and an improved center-symmetric local binary pattern (CS-LBP) feature extraction algorithm to address these challenges. The system comprises a client, server, and third-party computation entity. The client first extracts a 256-dimensional feature vector from the iris image using the enhanced CS-LBP algorithm, improving the accuracy and robustness of feature representation. The feature vector is then encrypted using the Brakerski-Gentry-Vaikuntanathan (BGV) homomorphic encryption scheme and transmitted to the third-party computation entity. Given the computational demands of the BGV algorithm and the 256-dimensional feature vector, the inclusion of cloud computing significantly reduces the impact of these demands, enabling efficient feature matching and similarity calculations in the encrypted domain. This approach effectively prevents data breaches and misuse, safeguarding user privacy. Experiments conducted on multiple subsets of the CASIA-IrisV4 dataset under various conditions show that the proposed model excels across key performance metrics, with a recognition accuracy of 97.0%, a recall rate of 96.5%, an F1 score of 96.7%, and an area under the curve (AUC) of 0.990. While the system faces challenges in time efficiency, cloud computing integration helps mitigate these, resulting in an average feature extraction time of 0.6 seconds per image, encryption time of 0.3 seconds per feature vector, and homomorphic computation time of 2.0 seconds per match. This study demonstrates the system's superiority in maintaining recognition accuracy and improving computational efficiency while protecting user privacy. Beyond its potential in iris recognition, it offers a novel solution for privacy protection in other biometric applications. Future research will focus on further optimizing the computational efficiency of the encryption algorithm, expanding the model's applicability to different biometric domains, and exploring efficient implementations in cloud computing environments.