论文标题
近乎收集及其对生物识别安全性的影响
Near-collisions and their Impact on Biometric Security
论文作者
论文摘要
生物识别识别包括两种操作模式。第一个是生物识别识别，它在于基于其生物识别技术确定个体的身份，并需要浏览整个数据库（即a 1：n搜索）。另一个是生物识别验证，对应于验证个人（即1：1搜索）以对其进行身份验证或授予她对某些服务的访问权限。匹配过程基于新鲜和注册生物识别模板之间的相似性。考虑到二进制模板的情况，我们研究了填充的数据库如何产生接近收费，从而影响了两种操作模式的安全性。通过在模板的大小上建立一个下限，并根据安全参数建立模板大小的大小和上限，从而洞悉二进制模板的安全性。我们提供有效的算法，用于分区泄漏的模板数据库，以改善主板设置的生成，该模板可以模仿任何已注册的用户以及可能未来的用户。最终通过实验研究强调了所提出的算法的实际影响。
Biometric recognition encompasses two operating modes. The first one is biometric identification which consists in determining the identity of an individual based on her biometrics and requires browsing the entire database (i.e., a 1:N search). The other one is biometric authentication which corresponds to verifying claimed biometrics of an individual (i.e., a 1:1 search) to authenticate her, or grant her access to some services. The matching process is based on the similarities between a fresh and an enrolled biometric template. Considering the case of binary templates, we investigate how a highly populated database yields near-collisions, impacting the security of both the operating modes. Insight into the security of binary templates is given by establishing a lower bound on the size of templates and an upper bound on the size of a template database depending on security parameters. We provide efficient algorithms for partitioning a leaked template database in order to improve the generation of a master-template-set that can impersonates any enrolled user and possibly some future users. Practical impacts of proposed algorithms are finally emphasized with experimental studies.
