In this paper, we study fundamental trade-offs in privacy-preserving biometric identification systems with noisy enrollment. The proposed identification systems include helper data, secret keys, and private keys. Helper data are stored in a public database and used for identification. Secret keys are either stored in a secure database or provided to the user, and can be used in a next step, e.g. for authentication. Private keys are provided by users, and are also used for identification. In this paper, we impose a noisy enrollment channel and an arbitrarily small privacy and secrecy leakage rate. We characterize the optimal trade-off among the identification, secret key, private key, and helper data rates. Depending on how secret keys are produced, we study two cases of the proposed privacy-preserving identification systems, where the secret keys are generated and chosen respectively. By introducing private keys, it is shown that the identification system achieves close to zero privacy leakage rate in both generated and chosen secret key settings. The results also show that the identification rate and the secret key rate can be enlarged by increasing the private key rate. This work provides a framework for analyzing privacy-preserving identification systems and an insight on the design of optimal systems.

中文翻译：

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带有嘈杂注册的隐私保护识别系统

在本文中，我们研究了具有噪声注册的隐私保护生物识别系统的基本权衡。提议的识别系统包括助手数据、秘密密钥和私人密钥。助手数据存储在公共数据库中并用于识别。密钥要么存储在安全数据库中，要么提供给用户，并可在下一步中使用，例如用于身份验证。私钥由用户提供，也用于身份识别。在本文中，我们强加了一个嘈杂的注册通道和一个任意小的隐私和保密泄漏率。我们描述了标识、密钥、私钥和辅助数据速率之间的最佳权衡。根据密钥的产生方式，我们研究了所提出的隐私保护识别系统的两种情况，生成 和 选择分别。通过引入私钥，表明身份识别系统在两种情况下都实现了接近于零的隐私泄漏率。生成 和 选择密钥设置。结果还表明，提高私钥率可以提高识别率和密钥率。这项工作提供了一个分析隐私保护识别系统的框架，并提供了对最佳系统设计的见解。