Biometric key generation based on generated intervals and two-layer error correcting technique

Wang, Peiyi; You, Lin; Hu, Gengran; Hu, Liqin; Jian, Zhihua; Xing, Chaoping

doi:10.1016/j.patcog.2020.107733

Cited by 25 publications

(29 citation statements)

References 17 publications

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“…Approaches for generating cryptographic key from biometrics have considered various biometric traits, such as fingerprint [12][13][14][15][16][17] finger vein [18], iris [19], face [20,21], voice [22], handwritten signature [23], gait [24][25][26][27], ECG [28,29], retina [30] and multi-modal [31][32][33].…”

Section: Fig 1 Generation Of Cryptographic Keys From Biometricsmentioning

confidence: 99%

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Two-factor-based RSA key generation from fingerprint biometrics and password for secure communication

Suresh

Pal

Balasundaram

2022

Complex Intell. Syst.

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In an asymmetric-key cryptosystem, the secure storage of private keys is a challenging task. This paper proposes a novel approach for generating the same public and private key pair on a need basis. Hence, the need for secure storage of the private key is done away with. The proposed approach for generating the key pair is based on two factors: fingerprint biometrics and password. A stable binary string is generated from the distances among pairs of minutiae points in a fingerprint using a gray code-based method. Experiments show that gray code representation significantly reduces the number of inconsistencies between the generated bit strings from two instances of the same fingerprint as against the binary code representation. Hence, the Reed–Solomon error correction code successfully corrects errors due to variations in multiple instances of the same fingerprint to induce stability in the generated string. Hash of the stable string generated from the fingerprint and the string generated from hashed password are XORed to derive a stable seed value. The proposed approach uses this seed value to generate two large prime numbers. These prime numbers are used to generate the public and private key pair using the RSA key generation method. This seed value ensures the generation of the same key pair every time. The experimental results show that the proposed approach can ensure a stable generation of the key. It is not required to store either the fingerprint template or the password. Moreover, the generated private key is also not stored. It can be regenerated on a need basis.

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Section: Fig 1 Generation Of Cryptographic Keys From Biometricsmentioning

confidence: 99%

“…In [17], feature distance set is generated by calculating distances between every pair of minutiae points. A generated intervals are constructed from the feature distance set.…”

Section: Fig 1 Generation Of Cryptographic Keys From Biometricsmentioning

confidence: 99%

Two-factor-based RSA key generation from fingerprint biometrics and password for secure communication

Suresh

Pal

Balasundaram

2022

Complex Intell. Syst.

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“…There are also biometric key generation techniques for biometric template protection [ 14 , 15 , 16 ]. Using these methods, a unique and high-entropy key can be generated from the user’s biometric input on the fly.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the electrocardiogram-based biometric key generation method in [ 14 ] requires for helper data to be stored. According to [ 16 ], the use of helper data is not desirable. The electroencephalography (EEG)-based method in [ 15 ] requires high-bandwidth data with more than 60 channels.…”

Section: Introductionmentioning

confidence: 99%

“…It was pointed out in [ 17 ] that in most commercially available EEG devices, less than 20 channels are provided. The fingerprint-based biometric key generation method in [ 16 ] requires an additional device such as a smart card. On the contrary, if a remote server is available, storing the biometric template on the remote server outside the device is another option for biometric verification without the need for additional tools or performance degradation [ 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

Jeon

Lee

2021

Sensors

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With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method.

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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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Biometric key generation based on generated intervals and two-layer error correcting technique

Cited by 25 publications

References 17 publications

Two-factor-based RSA key generation from fingerprint biometrics and password for secure communication

Two-factor-based RSA key generation from fingerprint biometrics and password for secure communication

Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

Fingerprint bio‐key generation based on a deep neural network

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