Cancelable biometric authentication leveraging empirical mode decomposition and quaternion representations for IoT...

Area of Science:

• Computer Science
• Cybersecurity
• Signal Processing

Background:

• Biometric authentication is critical for Internet of Things (IoT) security.
• Existing biometric data is vulnerable to breaches, necessitating advanced privacy preservation techniques.
• Cancelable biometric systems offer a solution by creating non-reversible templates.

Purpose of the Study:

• To develop an innovative method for generating cancelable biometric templates.
• To enhance user privacy and security in IoT environments.
• To ensure template diversity and system efficiency for IoT applications.

Main Methods:

• Integration of Empirical Mode Decomposition (EMD) for biometric data disaggregation into Intrinsic Mode Functions (IMFs).
• Application of quaternion mathematics to ensure template safety and non-reproducibility.
• Experimental assessment of the proposed method's performance and robustness.

Main Results:

• The proposed method achieves high accuracy with an Area Under the Receiver Operating Characteristic curve (AROC) of 0.9997.
• An extremely low Equal Error Rate (EER) demonstrates the system's reliability.
• The system exhibits minimal computational cost, making it suitable for resource-constrained IoT devices.

Conclusions:

• The novel cancelable biometric template generation method effectively addresses significant security challenges in IoT.
• The approach provides robust, privacy-preserving authentication with high performance.
• The method's efficiency and low computational overhead are ideal for practical IoT deployments.