Biometric Identification Systems With Noisy Enrollment for Gaussian Source
This work addresses theoretical limits for biometric security systems, providing insights for researchers in information theory and biometrics, but it is incremental as it builds on known results.
The paper investigates the fundamental trade-offs among identification, secrecy, storage, and privacy-leakage rates in biometric identification systems for Gaussian sources with noisy enrollment, deriving the capacity region and showing through numerical examples that achieving high secrecy and low privacy-leakage simultaneously is challenging.
In the present paper, we investigate the fundamental trade-off of identification, secrecy, storage, and privacy-leakage rates in biometric identification systems for hidden or remote Gaussian sources. We introduce a technique for deriving the capacity region of these rates by converting the system to one where the data flow is in one-way direction. Also, we provide numerical calculations of three different examples for the generated-secret model. The numerical results imply that it seems hard to achieve both high secrecy and small privacy-leakage rates simultaneously. In addition, as special cases, the characterization coincides with several known results in previous studies.