Unpredictability Assessment of Biometric Hashing Under Naive and Advanced Threat Conditions

Recent years have witnessed the use of biometric recognition systems in increasing number of applications with the number of users growing at a steady pace. However, security and privacy problems have arisen from this upsurge of interest to biometric systems. Template protection methods solve such security and privacy problems where unpredictability is a crucial goal. Here, we study the unpredictability of biohashing (a transformation-based template protection method) using entropy as a measure. Our novel work outlines a systematic approach for theoretical evaluation of biohashes using estimated entropy which is based on degree of freedom of Binomial distribution. Our experiments demonstrate that biohash unpredictability varies in different threat models where the entropy of a biohash is almost equal to its bit length under the naive scenario and is significantly low in the advanced scenario, implying that the amount of information kept hidden in a biohash is more likely to be predicted.