Improving the Efficiency of Capture-Resistant Biometric Authentication Based on Set Intersection

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 could speed up computation as many as 2.29 times 10⁶ times.