AMB-FHE：基于全同态加密的自适应多生物特征融合
AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption
- Hochschule Darmstadt（达姆施塔特应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出AMB-FHE方法，通过全同态加密对多生物特征参考模板进行联合加密，在运行时自适应安全需求，提升了隐私保护与认证灵活性。
AI中文摘要：
生物特征系统致力于在安全性与可用性之间取得平衡。对于高安全性应用，通常建议采用结合多种生物特征模态的多生物特征系统。然而，展示多种生物特征模态可能会损害整体系统的用户友好性，且在所有情况下也并非必要。在本研究中，我们提出了一种简单而灵活的方法，以增强同态加密多生物特征参考模板的隐私保护，同时支持在运行时适应安全需求：一种基于全同态加密的自适应多生物特征融合（AMB-FHE）。AMB-FHE 在由 CASIA 虹膜和 MCYT 指纹数据集组成的双模态生物特征数据库上进行了基准测试，并使用深度神经网络进行特征提取。我们的贡献易于实现，提高了生物特征认证的灵活性，同时通过对来自多种模态的模板进行联合加密提供了更强的隐私保护。
英文摘要：
Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, we present a simple but flexible approach to increase the privacy protection of homomorphically encrypted multi-biometric reference templates while enabling adaptation to security requirements at run-time: An adaptive multi-biometric fusion with fully homomorphic encryption (AMB-FHE). AMB-FHE is benchmarked against a bimodal biometric database consisting of the CASIA iris and MCYT fingerprint datasets using deep neural networks for feature extraction. Our contribution is easy to implement and increases the flexibility of biometric authentication while offering increased privacy protection through joint encryption of templates from multiple modalities.
