Towards Practical Biometric Key Generation with Randomized Biometric Templates
Lucas Ballard,
Seny Kamara,
Fabian Monrose,
and Mike Reiter
Abstract
Although biometrics have garnered significant interest as a source of
entropy for cryptographic key generation, recent studies indicate that
many biometric modalities may not actually offer enough uncertainty
for this purpose. In this paper, we exploit a novel source of entropy
that can be used with any biometric modality but that has yet to be
utilized for key generation, namely associating uncertainty with the way in
which the biometric input is measured. Our construction
poses only a modest requirement on a user: the
ability to remember a low-entropy password. We identify the technical
challenges of this approach, and develop novel techniques to
overcome these difficulties. Our analysis of this approach indicates
that it may offer the potential to generate stronger keys: In our experiments,
40% of the users are able to generate keys that are at least 2
30
times stronger than passwords alone.
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