1 research outputs found
Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication
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.Comment: 8 pages, 5 figure