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학술지 Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing
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저자
Zhe Jin, 황정연, Yen-Lung Lai, 김수형, Andrew Beng Jin Teoh
발행일
201802
출처
IEEE Transactions on Information Forensics and Security, v.13 no.2, pp.393-407
ISSN
1556-6013
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TIFS.2017.2753172
협약과제
17HH2100, 비대면 본인확인을 위한 바이오 공개키 기반구조 기술 개발, 조상래
초록
In this paper, we propose a ranking-based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed 'Index-of-Max' (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely, Gaussian random projection-based and uniformly random permutation-based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoys several merits. First, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Second, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Third, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.
키워드
cancelable template, Fingerprint, Index-of-Max hashing, security and privacy
KSP 제안 키워드
Accuracy performance, Biometric features, Biometric information, Biometric template protection, Cancelable template, Feature Vector, Feature alignment, Locality sensitive hashing, Privacy attacks, Random parameters, Random permutation