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Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication
arXiv - CS - Human-Computer Interaction Pub Date : 2020-03-18 , DOI: arxiv-2003.08433
Abhishek Jana, Md Kamruzzaman Sarker, Monireh Ebrahimi, Pascal Hitzler, George T Amariucai

Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data, thus exposing users' credentials to falsification. In this paper, we introduce a secure way to handle user-specific information involved with the use of vector-space classifiers or artificial neural networks for biometric authentication. Our proposed architecture, called a Neural Fuzzy Extractor (NFE), allows the coupling of pre-existing classifiers with fuzzy extractors, through a artificial-neural-network-based buffer called an expander, with minimal or no performance degradation. The NFE thus offers all the performance advantages of modern deep-learning-based classifiers, and all the security of standard fuzzy extractors. We demonstrate the NFE retrofit to a classic artificial neural network for a simple scenario of fingerprint-based user authentication.

中文翻译:

神经模糊提取器:一种使用人工神经网络进行生物特征用户认证的安全方法

在传感器开发和人工智能的新进展、计算成本的下降以及手持计算设备的普及的推动下,生物特征用户身份验证(和识别)正在迅速普及。现代生物特征认证方法基于复杂的机器学习技术,无法避免存储经过训练的分类器详细信息或明确的用户生物特征数据,从而使用户的凭据容易被伪造。在本文中,我们介绍了一种安全的方法来处理涉及使用向量空间分类器或人工神经网络进行生物特征认证的用户特定信息。我们提出的架构,称为神经模糊提取器(NFE),允许将预先存在的分类器与模糊提取器耦合,通过称为扩展器的基于人工神经网络的缓冲区,性能下降最小或没有。因此,NFE 提供了现代基于深度学习的分类器的所有性能优势,以及标准模糊提取器的所有安全性。我们演示了对经典人工神经网络的 NFE 改造,用于基于指纹的用户身份验证的简单场景。
更新日期:2020-03-20
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