Dual Fingerprints Fusion for Cryptographic Key Generation

Marimuthu, Mudaray; Kannammal, A.

doi:10.5120/21865-5194

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…In 2015, Balamurugan et al fused the face and iris at the feature level, obtained the user's multimodal biometric template, and generated a secure biometric key based on the template, in which the length could reach 256 bits 14 . In the same year, Marimuthu and Kannammal proposed a dual fingerprint fusion key generation scheme, 15 which increased the number of available fingerprint feature points and used fingerprint feature level fusion to generate user security keys. However, the scheme needs to store the location template of the corresponding fingerprint detail points; thus, the problem regarding the security of the original biological data still exists.…”

Section: Related Workmentioning

confidence: 99%

Fingerprint bio‐key generation based on a deep neural network

Kang

et al. 2021

Int J of Intelligent Sys

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With the increasing use of biometric identity authentication, biological key generation technology is receiving much attention. A high-strength key that is easy to store and manage can be generated from biological characteristics, which can improve the convenience and security of user-encryption operations. However, the generation of a high-strength, stable, and robust key using the currently available fingerprint bio-key generation technology is difficult. This paper proposes a three-layer framework for fingerprint bio-key generation that is composed of a fingerprint bio-key preprocessor, fingerprint bio-key stabilizer (FPBK_Stabilizer), and fingerprint bio-key fuzzy extractor. In the FPBK_Stabilizer, feature selection and layer-by-layer convolution projection characteristics from deep neural networks are used to effectively eliminate the instability between fingerprint samples. Furthermore, a suitable multilayer convolutional projection fingerprint bio-key generation model is designed for generating the fingerprint bio-key. The results of a fingerprint bio-key generation experiment involving a fingerprint library comprising 100 people verified the efficacy of the proposed framework. Specifically, the proposed framework exhibited a generation intensity >1024 bits, accuracy rate >98.0%, and misrecognition rate <1.5%, thereby verifying its high-strength, stable, and robust fingerprint bio-key generation capability.

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Section: Related Workmentioning

confidence: 99%

Fingerprint bio‐key generation based on a deep neural network

Kang

et al. 2021

Int J of Intelligent Sys

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“…The resulting centroid is used as key for AES. The authors of [33] propose a multi-biometric system using feature level fusion from two different fingers as input for encryption algorithms. They system is tested on RSA, DES, 3DES and a Proposed Approach.…”

Section: Literature Reviewmentioning

confidence: 99%

User-Centric Key Entropy: Study of Biometric Key Derivation Subject to Spoofing Attacks

Dinca

Hancke

2017

Entropy

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Biometric data can be used as input for PKI key pair generation. The concept of not saving the private key is very appealing, but the implementation of such a system shouldn't be rushed because it might prove less secure then current PKI infrastructure. One biometric characteristic can be easily spoofed, so it was believed that multi-modal biometrics would offer more security, because spoofing two or more biometrics would be very hard. This notion, of increased security of multi-modal biometric systems, was disproved for authentication and matching, studies showing that not only multi-modal biometric systems are not more secure, but they introduce additional vulnerabilities. This paper is a study on the implications of spoofing biometric data for retrieving the derived key. We demonstrate that spoofed biometrics can yield the same key, which in turn will lead an attacker to obtain the private key. A practical implementation is proposed using fingerprint and iris as biometrics and the fuzzy extractor for biometric key extraction. Our experiments show what happens when the biometric data is spoofed for both uni-modal systems and multi-modal. In case of multi-modal system tests were performed when spoofing one biometric or both. We provide detailed analysis of every scenario in regard to successful tests and overall key entropy. Our paper defines a biometric PKI scenario and an in depth security analysis for it. The analysis can be viewed as a blueprint for implementations of future similar systems, because it highlights the main security vulnerabilities for bioPKI. The analysis is not constrained to the biometric part of the system, but covers CA security, sensor security, communication interception, RSA encryption vulnerabilities regarding key entropy, and much more.

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“…Most of these models are simple mapping or linking methods, similar to BiometricRSA. It is not easy to find a representative study because their proposals do not provide a reasonable and concrete method [18][19][20][21]. Minhye Seo et al proposed a new biometric-based key derivation function for enhancement of the authentication system by the replacement of password-based key derivation functions [22].…”

Section: Introductionmentioning

confidence: 99%

PCA-CIA Ensemble-based Feature Extraction for Bio-Key Generation

2020

KSII TIIS

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Post-Quantum Cryptography (PQC) is rapidly developing as a stable and reliable quantum-resistant form of cryptography, throughout the industry. Similarly to existing cryptography, however, it does not prevent a third-party from using the secret key when third party obtains the secret key by deception, unauthorized sharing, or unauthorized proxying. The most effective alternative to preventing such illegal use is the utilization of biometrics during the generation of the secret key. In this paper, we propose a biometric-based secret key generation scheme for multivariate quadratic signature schemes, such as Rainbow. This prevents the secret key from being used by an unauthorized third party through biometric recognition. It also generates a shorter secret key by applying Principal Component Analysis (PCA)-based Confidence Interval Analysis (CIA) as a feature extraction method. This scheme's optimized implementation performed well at high speeds.

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Dual Fingerprints Fusion for Cryptographic Key Generation

Cited by 7 publications

References 9 publications

Fingerprint bio‐key generation based on a deep neural network

Fingerprint bio‐key generation based on a deep neural network

User-Centric Key Entropy: Study of Biometric Key Derivation Subject to Spoofing Attacks

PCA-CIA Ensemble-based Feature Extraction for Bio-Key Generation

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