Secure Authentication with Noisy Sources

Fuzzy extractor provides a way for key generation from biometrics and other noisy data. It has been widely applied in biometric authentication systems that provides natural and passwordless user authentication. In general, given a random sample, a fuzzy extractor extracts a nearly uniform random string, and subsequently regenerates the string using a different yet similar noisy sample. However, due to error tolerance between the two samples, fuzzy extractor imposes high information (entropy) loss and thus, it only works for an input with high enough entropy. In this thesis, we aimed to show security over low entropy sources and enable higher achievable error correction capacity for better noise tolerance over the fuzzy extractor. The findings of our research can be used to support secure authentication with large class of natural available noisy sources via fuzzy extractor.