RSA Key Generation from Cancelable Fingerprint Biometrics

Sarkar, Arpita; Singh, Binod Kr; Bhaumik, Ujjayanta

doi:10.1109/iccubea.2017.8463959

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…Here, several existing approaches of stable binary string generations are briefly discussed. The generated stable binary string can be used directly as a cryptographic key for symmetric encipherment [12][13][14][15] or it can be used as a seed value to generate the private-public key pairs for asymmetric encipherment [34,35].…”

Section: Fig 1 Generation Of Cryptographic Keys From Biometricsmentioning

confidence: 99%

“…Very few works has been proposed a framework for generating private-public key pairs for secure exchanging of session key parameters and digitally signing electronic messages. In [34], the authors proposed a technique to generate a pair of private and public key for RSA cryptosystem using fingerprint biometrics. Minutiae points are extracted from the fingerprint biometrics and generated a cancellable template by shuffling the x and y coordinate values and concatenated by performing bitwise XOR operation among the x and y coordinates of each minutiae point.…”

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%

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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“…Ranjan et al [38] introduced a key generation approach based on the distance to reduce some complex operations for generating the bio-key. Sarkar et al [39] gave a cancelable key generation approach for asymmetric cryptography. Specifically, they adopted a transformation method based on shuffling to generate the revocable bio-key.…”

Section: Key Generation Scheme Based On Biometricsmentioning

confidence: 99%

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Wang

Zhang

et al. 2021

Applied Sciences

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Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

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“…Biometric systems that mix statistics with cryptography key generation are referred to as Bio-key generation. To ensure secure transmission, cryptological key plays a serious role [9,10]. If we have a tendency to introduce biometric with the key, it guarantee a lot of authentication throughout transmission.…”

Section: A Backgroundmentioning

confidence: 99%

Bio Key Generation for Integrity Checking Using Elliptic Curves in Fingerprint Verification System

Rekha*,

Kavitha

2019

IJEAT

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This paper proposes a Bio-key generation method for integrity checking in fingerprint verification system. A major drawback in traditional cryptography is random key generation and those keys need to be remembered by the user. To overcome such drawback the crypto-Bio-key generation system is introduced. In this system, there are three process to follow. Initially, a revocable transformation of the compressed fingerprint feature data using transformation key for security enhancement. Secondly, the key exchange technique using Elliptic curve cryptography (ECC) to generate secret key. Finally SHA256 hash function generates the Bio-key for high level integrity checking.

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RSA Key Generation from Cancelable Fingerprint Biometrics

Cited by 6 publications

References 8 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

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Bio Key Generation for Integrity Checking Using Elliptic Curves in Fingerprint Verification System

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