Improving the Efficiency of Capture-resistant Biometric Authentication based on Set Intersection

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Xunhua Wang
James Madison University
United States

Philip Huff
James Madison University
United States

Brett Tjaden
James Madison University
United States

Abstract:
Traditional biometric authentication systems store biometric reference templates in cleartext on an authentication server, making them vulnerable to theft. Fuzzy extractors allow an authentication server to store biometric verification data that are resistant to capture. It is hard to recover the reference templates from these biometric verification data, thus increasing the privacy of the reference templates. In this paper, we improve the efficiency of a set intersection-based fuzzy extractor in two ways. First, we speed up the computation of verifying a biometric sample under some parameter combinations through integrating a Reed-Solomon decoding algorithm. Second, we propose a new function to improve the storage efficiency of the fuzzy extractor. A prototype implementation is developed to validate our improvements and it shows that our first improvement speeds up computation as many as 2.29 * 10^6 times.