论文标题
高斯来源的生物特征识别系统具有嘈杂的入学率
Biometric Identification Systems With Noisy Enrollment for Gaussian Source
论文作者
论文摘要
在本文中，我们调查了隐藏或远程高斯来源的生物识别系统中的身份，保密，存储和隐私率率的基本权衡。我们引入了一种通过将系统转换为单向方向的技术来推导这些速率的能力区域的技术。另外，我们为生成的秘密模型提供了三个不同示例的数值计算。数值结果表明，同时同时达到高保密和较小的隐私率似乎很难。此外，作为特殊情况，表征与先前研究中的几个已知结果一致。
In the present paper, we investigate the fundamental trade-off of identification, secrecy, storage, and privacy-leakage rates in biometric identification systems for hidden or remote Gaussian sources. We introduce a technique for deriving the capacity region of these rates by converting the system to one where the data flow is in one-way direction. Also, we provide numerical calculations of three different examples for the generated-secret model. The numerical results imply that it seems hard to achieve both high secrecy and small privacy-leakage rates simultaneously. In addition, as special cases, the characterization coincides with several known results in previous studies.
