A linear convolution-based cancelable fingerprint biometric authentication system

2023

Preprint

With the development of the information era, the study of biometric keys has attracted increasing attention. However, the performance and safety of the proposed methods seem challenging to meet the needs of real applications. To solve this problem, this paper presents a finger vein biometric key system. The proposed system proposes an improved network in the feature extraction stage, which combines the coordinate attention mechanism and SkipNet to obtain richer feature information. An improved, reliable feature selection method is introduced to acquire more discriminative finger vein features. A Lagrange interpolation method then binds the user's finger vein features and keys. Finally, experiments are carried out on the public USM, Poly, and Net64 databases. The results indicate that we acquire a 1024-bit key with 0.049% FAR and 0% FRR in Net64 and 0% FAR and FRR in USM and Poly. Additionally, the system's security is analyzed from key randomness, system information leak attack, brute force attack, cross-matching attack, and spoofing attack. Experimental and theoretical analyses show that the proposed system has good accuracy and security.

Section: Performance Evaluation Metricsmentioning

confidence: 99%

A Secure Biometric Key Generation Based on Coordinate Attention Mechanism And Reliable Feature Selection

Zhou

2023

Preprint

“…With XOR encryption, the key is not stored in its original form. Yang et al [ 23 ] designed a linear convolution-based cancelable fingerprint authentication system. The proposed system uses a help vector as the second input to the linear convolution function so as to increase the immunity of resultant templates to errors from the feature data.…”

Section: Related Workmentioning

confidence: 99%

A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks

Bedari

Yang

2022

Sensors

Self Cite

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.

“…Such a transformation is intentional and reproducible [ 7 ]. An essential property of cancelable biometrics is irreversibility, meaning that it should be computationally infeasible to retrieve the original biometric data from the transformed template [ 8 ]. In the verification phase, the same transformation is applied to the query data.…”

Section: Introductionmentioning

confidence: 99%

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Yang

Cui

et al. 2023

Sensors

Self Cite

The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.