Yonsei University · 情報科学
Professor Andrew Beng Jin Teoh's research lab specializes in privacy-preserving biometric systems, focusing on developing cancelable and revocable biometric template protection techniques to address the inherent risks of permanent data compromise. The lab explores advanced cryptographic and signal processing methods—such as biometric hashing, fuzzy commitment schemes, and randomized quantization—to ensure non-invertibility and user-specific security. A central theme is enabling biometric systems that are both secure and revocable, allowing users to regenerate new biometric templates if compromised, thus mitigating long-term identity theft risks. The lab also investigates keystroke dynamics and facial biometrics for practical deployment in real-world authentication systems.
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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
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