TY - GEN
T1 - Biometrics-based identifiers for digital identity management
AU - Bhargav-Spantzel, Abhilasha
AU - Squicciarini, Anna
AU - Bertino, Elisa
AU - Kong, Xiangwei
AU - Zhang, Weike
PY - 2010
Y1 - 2010
N2 - We present algorithms to reliably generate biometric identifiers from a user's biometric image which in turn is used for identity verification possibly in conjunction with cryptographic keys. The biometric identifier generation algorithms employ image hashing functions using singular value decomposition and support vector classification techniques. Our algorithms capture generic biometric features that ensure unique and repeatable biometric identifiers. We provide an empirical evaluation of our techniques using 2569 images of 488 different individuals for three types of biometric images; namely fingerprint, iris and face. Based on the biometric type and the classification models, as a result of the empirical evaluation we can generate biometric identifiers ranging from 64 bits up to 214 bits. We provide an example use of the biometric identifiers in privacy preserving multi-factor identity verification based on zero knowledge proofs. Therefore several identity verification factors, including various traditional identity attributes, can be used in conjunction with one or more biometrics of the individual to provide strong identity verification. We also ensure security and privacy of the biometric data. More specifically, we analyze several attack scenarios. We assure privacy of the biometric using the one-way hashing property, in that no information about the original biometric image is revealed from the biometric identifier.
AB - We present algorithms to reliably generate biometric identifiers from a user's biometric image which in turn is used for identity verification possibly in conjunction with cryptographic keys. The biometric identifier generation algorithms employ image hashing functions using singular value decomposition and support vector classification techniques. Our algorithms capture generic biometric features that ensure unique and repeatable biometric identifiers. We provide an empirical evaluation of our techniques using 2569 images of 488 different individuals for three types of biometric images; namely fingerprint, iris and face. Based on the biometric type and the classification models, as a result of the empirical evaluation we can generate biometric identifiers ranging from 64 bits up to 214 bits. We provide an example use of the biometric identifiers in privacy preserving multi-factor identity verification based on zero knowledge proofs. Therefore several identity verification factors, including various traditional identity attributes, can be used in conjunction with one or more biometrics of the individual to provide strong identity verification. We also ensure security and privacy of the biometric data. More specifically, we analyze several attack scenarios. We assure privacy of the biometric using the one-way hashing property, in that no information about the original biometric image is revealed from the biometric identifier.
UR - http://www.scopus.com/inward/record.url?scp=77953989603&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953989603&partnerID=8YFLogxK
U2 - 10.1145/1750389.1750401
DO - 10.1145/1750389.1750401
M3 - Conference contribution
AN - SCOPUS:77953989603
SN - 9781605588957
T3 - ACM International Conference Proceeding Series
SP - 84
EP - 96
BT - IDtrust2010 - Proceedings of the 9th Symposium on Identity and Trust on the Internet
T2 - 9th Symposium on Identity and Trust on the Internet, IDtrust 2010
Y2 - 13 April 2010 through 15 April 2010
ER -