TY - GEN
T1 - Lattice-Based Secure Biometric Authentication for Hamming Distance
AU - Cheon, Jung Hee
AU - Kim, Dongwoo
AU - Kim, Duhyeong
AU - Lee, Joohee
AU - Shin, Junbum
AU - Song, Yongsoo
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Biometric authentication is a protocol which verifies a user’s authority by comparing her biometric with the pre-enrolled biometric template stored in the server. Biometric authentication is convenient and reliable; however, it also brings privacy issues since biometric information is irrevocable when exposed. In this paper, we propose a new user-centric secure biometric authentication protocol for Hamming distance. The biometric data is always encrypted so that the verification server learns nothing about biometric information beyond the Hamming distance between enrolled and queried templates. To achieve this, we construct a single-key function-hiding inner product functional encryption for binary strings whose security is based on a variant of the Learning with Errors problem. Our protocol consists of a single round, and is almost optimal in the sense that its time and space complexity grow quasi-linearly with the size of biometric templates. On implementation with concrete parameters, for binary strings of size ranging from 579 to 18,229 bytes (according to NIST IREX IX report), our scheme outperforms previous work from the literature.
AB - Biometric authentication is a protocol which verifies a user’s authority by comparing her biometric with the pre-enrolled biometric template stored in the server. Biometric authentication is convenient and reliable; however, it also brings privacy issues since biometric information is irrevocable when exposed. In this paper, we propose a new user-centric secure biometric authentication protocol for Hamming distance. The biometric data is always encrypted so that the verification server learns nothing about biometric information beyond the Hamming distance between enrolled and queried templates. To achieve this, we construct a single-key function-hiding inner product functional encryption for binary strings whose security is based on a variant of the Learning with Errors problem. Our protocol consists of a single round, and is almost optimal in the sense that its time and space complexity grow quasi-linearly with the size of biometric templates. On implementation with concrete parameters, for binary strings of size ranging from 579 to 18,229 bytes (according to NIST IREX IX report), our scheme outperforms previous work from the literature.
KW - Biometric authentication
KW - Inner product functional encryption
KW - Learning with errors
UR - http://www.scopus.com/inward/record.url?scp=85120069373&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90567-5_33
DO - 10.1007/978-3-030-90567-5_33
M3 - Conference contribution
AN - SCOPUS:85120069373
SN - 9783030905668
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 653
EP - 672
BT - Information Security and Privacy - 26th Australasian Conference, ACISP 2021, Proceedings
A2 - Baek, Joonsang
A2 - Ruj, Sushmita
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th Australasian Conference on Information Security and Privacy, ACISP 2021
Y2 - 1 December 2021 through 3 December 2021
ER -