Andrew Beng Jin Teoh
연세대학교 전기전자공학과 · 컴퓨터과학
Andrew Beng Jin Teoh 교수의 연구실은 생체인식 기반 신원 확인 기술의 보안과 개인정보 보호 문제를 해결하기 위한 캔슬러블 생체인식 기술을 핵심으로 연구하고 있습니다. 특히 생체정보의 불가재성으로 인한 영구적 유출 위험을 해결하기 위해, 비밀번호나 토큰 기반의 난수를 활용해 생체정보를 변형하는 기법을 개발하며, 복구 가능하고 비가역적인 템플릿 보호 기법을 중심으로 연구를 전개하고 있습니다. 이는 생체정보를 재사용할 수 있도록 허용하는 '템플릿 재발급' 메커니즘을 통해 기존 생체인식 기술의 보안 한계를 극복하고자 합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the i
Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up
Biometric characteristics cannot be changed; therefore, the loss of privacy is permanent if they are ever compromised. This paper presents a two-factor cancelable formulation, where the biometric data are distorted in a revocable but non-reversible manner by first transforming the raw biometric data into a fixed-length feature vector and then projecting the feature vector onto a sequence of random subspaces that were derived from a user-specific pseudorandom number (PRN). This process is revocab
