Biometric Identification Systems With Noisy Enrollment for Gaussian Source

TL;DR

This paper explores the fundamental limits of biometric identification systems with noisy enrollment for Gaussian sources, focusing on the trade-offs between identification, secrecy, storage, and privacy leakage, and provides a method to derive their capacity region.

Contribution

It introduces a technique to derive the capacity region of biometric systems with noisy enrollment by converting the system into a one-way data flow model, and offers numerical examples illustrating these trade-offs.

Findings

High secrecy and low privacy leakage are hard to achieve simultaneously.

The capacity region characterization aligns with known results in special cases.

Numerical examples demonstrate the trade-offs in generated-secret models.

Abstract

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.