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Coding solutions for the secure biometric storage problem


Schipani, D; Rosenthal, J (2010). Coding solutions for the secure biometric storage problem. In: Greferath, M; Rosenthal, J. Information Theory Workshop (ITW), 2010 IEEE. Dublin: IEEE, 1-4.

Abstract

The paper studies the problem of securely storing biometric passwords, such as fingerprints and irises. With the help of coding theory Juels and Wattenberg derived in 1999 a scheme where similar input strings will be accepted as the same biometric. In the same time nothing could be learned from the stored data. They called their scheme a fuzzy commitment scheme. In this paper we will revisit the solution of Juels and Wattenberg and we will provide answers to two important questions: what type of error-correcting codes should be used and what happens if biometric templates are not uniformly distributed, i.e. the biometric data come with redundancy. Answering the first question will lead us to the search for low-rate large-minimum distance error-correcting codes which come with efficient decoding algorithms up to the designed distance. In order to answer the second question we relate the rate required with a quantity connected to the “entropy” of the string, trying to estimate a sort of “capacity”, if we want to see a flavor of the converse of Shannon's noisy coding theorem. Finally we deal with side-problems arising in a practical implementation and we propose a possible solution to the main one that seems to have so far prevented in many situations real life applications of the fuzzy scheme.

Abstract

The paper studies the problem of securely storing biometric passwords, such as fingerprints and irises. With the help of coding theory Juels and Wattenberg derived in 1999 a scheme where similar input strings will be accepted as the same biometric. In the same time nothing could be learned from the stored data. They called their scheme a fuzzy commitment scheme. In this paper we will revisit the solution of Juels and Wattenberg and we will provide answers to two important questions: what type of error-correcting codes should be used and what happens if biometric templates are not uniformly distributed, i.e. the biometric data come with redundancy. Answering the first question will lead us to the search for low-rate large-minimum distance error-correcting codes which come with efficient decoding algorithms up to the designed distance. In order to answer the second question we relate the rate required with a quantity connected to the “entropy” of the string, trying to estimate a sort of “capacity”, if we want to see a flavor of the converse of Shannon's noisy coding theorem. Finally we deal with side-problems arising in a practical implementation and we propose a possible solution to the main one that seems to have so far prevented in many situations real life applications of the fuzzy scheme.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Language:English
Date:30 September 2010
Deposited On:02 Feb 2011 09:39
Last Modified:07 Dec 2017 05:42
Publisher:IEEE
ISBN:978-1-4244-8262-7 (P) 978-1-4244-8263-4 (E)
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/CIG.2010.5592899

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