用同态加密实现可动态调整的多模态生物特征融合，兼顾隐私与灵活性。
AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption
- 基于同态加密对多模态特征模板进行联合加密，支持运行时安全策略自适应。
- 在CASIA虹膜与MCYT指纹数据集上验证，模型准确率保持高位。
- 适合高安全性场景下的生物特征认证，尤其注重用户隐私保护的系统。
生物特征系统需在安全性和可用性间取得平衡。多模态生物特征系统因结合多种生物特征而常用于高安全场景，但多重特征输入可能降低用户体验，且并非所有情况都必需。本文提出一种简单灵活的方法——自适应多模态生物特征融合与全同态加密（AMB-FHE），在保证同态加密下多模态参考模板隐私的前提下，支持运行时安全需求的动态调整。该方法在包含CASIA虹膜与MCYT指纹数据集的双模态数据库上，采用深度神经网络进行特征提取进行了基准测试。本方案易于实现，提升了生物特征认证的灵活性，并通过多模态模板的联合加密增强了隐私保护。
原文摘要 · Abstract (English)
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.
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