TY - GEN
T1 - On the guessability of binary biometric templates
T2 - 2017 IEEE International Joint Conference on Biometrics, IJCB 2017
AU - Mai, Guangcan
AU - Lim, Meng Hui
AU - Yuen, Pong C.
N1 - Funding Information:
This study was partially supported by a Hong Kong RGC grant (HKBU 12201414) and the Madam Kwok Chung Bo Fun Graduate School Development Fund, HKBU. The authors would like to thank Mr. Jiawei Li for his helpful suggestions.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - A security index for biometric systems is essential because biometrics have been widely adopted as a secure authentication component in critical systems. Most of bio-metric systems secured by template protection schemes are based on binary templates. To adopt popular template protection schemes such as fuzzy commitment and fuzzy extractor that can be applied on binary templates only, non-binary templates (e.g., real-valued, point-set based) need to be converted to binary. However, existing security measurements for binary template based biometric systems either cannot reflect the actual attack difficulties or are too computationally expensive to be practical. This paper presents an acceleration of the guessing entropy which reflects the expected number of guessing trials in attacking the binary template based biometric systems. The acceleration benefits from computation reuse and pruning. Experimental results on two datasets show that the acceleration has more than 6x, 20x, and 200x speed up without losing the estimation accuracy in different system settings.
AB - A security index for biometric systems is essential because biometrics have been widely adopted as a secure authentication component in critical systems. Most of bio-metric systems secured by template protection schemes are based on binary templates. To adopt popular template protection schemes such as fuzzy commitment and fuzzy extractor that can be applied on binary templates only, non-binary templates (e.g., real-valued, point-set based) need to be converted to binary. However, existing security measurements for binary template based biometric systems either cannot reflect the actual attack difficulties or are too computationally expensive to be practical. This paper presents an acceleration of the guessing entropy which reflects the expected number of guessing trials in attacking the binary template based biometric systems. The acceleration benefits from computation reuse and pruning. Experimental results on two datasets show that the acceleration has more than 6x, 20x, and 200x speed up without losing the estimation accuracy in different system settings.
UR - http://www.scopus.com/inward/record.url?scp=85046265499&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2017.8272719
DO - 10.1109/BTAS.2017.8272719
M3 - Conference proceeding
AN - SCOPUS:85046265499
T3 - IEEE International Joint Conference on Biometrics, IJCB 2017
SP - 367
EP - 374
BT - IEEE International Joint Conference on Biometrics, IJCB 2017
PB - IEEE
Y2 - 1 October 2017 through 4 October 2017
ER -