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首页> 外文会议>IEEE International Conference on Trust, Security and Privacy in Computing and Communications >Secure and Privacy Preserving Method for Biometric Template Protection using Fully Homomorphic Encryption
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Secure and Privacy Preserving Method for Biometric Template Protection using Fully Homomorphic Encryption

机译：使用完全同态加密的生物识别模板保护的安全和隐私保存方法

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摘要

The rapid proliferation of biometrics has led to growing concerns about the security and privacy of the biometric data (template). A biometric uniquely identifies an individual and unlike passwords, it cannot be revoked or replaced since it is unique and fixed for every individual. To address this problem, many biometric template protection methods using fully homomorphic encryption have been proposed. But, most of them (i) are computationally expensive and practically infeasible (ii) do not support operations over real valued biometric feature vectors without quantization (iii) do not support packing of real valued feature vectors into a ciphertext (iv) require multi-shot enrollment of users for improved matching performance. To address these limitations, we propose a secure and privacy preserving method for biometric template protection using fully homomorphic encryption. The proposed method is computationally efficient and practically feasible, supports operations over real valued feature vectors without quantization and supports packing of real valued feature vectors into a single ciphertext. In addition, the proposed method enrolls the users using one-shot enrollment. To evaluate the proposed method, we use three face datasets namely LFW, FEI and Georgia tech face dataset. The encrypted face template (for 128 dimensional feature vector) requires 32.8 KB of memory space and it takes 2.83 milliseconds to match a pair of encrypted templates. The proposed method improves the matching performance by ~3 % when compared to state-of-the-art, while providing high template security.
机译：生物识别性的快速增殖导致对生物识别数据（模板）的安全和隐私的担忧。生物识别唯一标识个人和与密码不同，因为它是唯一的，而且每个人都无法撤销或替换。为了解决这个问题，已经提出了许多使用完全同态加密的生物识别模板保护方法。但是，其中大多数（i）是计算昂贵的并且实际上不可行（ii）不支持通过型实值的生物识别特征向量的操作（iii）不支持将真实值的特征向量包装成密文（iv）需要多 - 拍摄用户提高匹配性能的注册。为了解决这些限制，我们提出了一种使用完全同态加密的生物识别模板保护的安全和隐私保存方法。所提出的方法是计算上有效的，实际上可行，支持在没有量化的实际值特征向量上的操作，并支持真实值的特征向量的包装到单个密文中。此外，所提出的方法使用单次注册注册用户。为了评估所提出的方法，我们使用三个脸部数据集即LFW，Fei和佐治亚州科技脸部数据集。加密的面部模板（对于128维特征向量）需要32.8 kB的内存空间，它需要2.83毫秒，以匹配一对加密模板。当与最先进的同时提供高模板安全性时，所提出的方法将匹配性能提高〜3％。

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