Cryptographic Key Generation Scheme from Cancellable Biometrics

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

doi:10.1007/978-981-10-7871-2_26

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Another key focus is the preservation of the privacy of biometric data used in cryptographic processes, achieved through cancelable biometrics, one-way transformations, and the avoidance of raw template storage [68,81]. Analyzing the security and revocability of systems in the event of template compromise is equally essential.…”

Section: Discussionmentioning

confidence: 99%

“…However, the challenge lies in the variability of biometrics, making it difficult to consistently generate keys [80], as well as the lack of in-depth security analysis against advanced threats [68,78]. Furthermore, reliance on specific modalities such as fingerprints or ECGs limits its applicability [81]. To enhance the generation of the biometric cryptographic key, researchers should explore techniques such as quantization, grey coding, or error correction codes to extract stable keys from noisy biometric signals [68], introduce cancelable biometrics to enable template renewal in case of compromise [78], and consider emerging wearable-friendly modalities such as EEGs, EMGs, skin conductance to overcome limitations associated with specific modalities [81].…”

Section: Cryptographic Key Generation Using Biometricsmentioning

confidence: 99%

“…Preserving user privacy through cancelable biometrics is also crucial [132]. It is essential to protect the privacy of biometric data used in key generation to prevent exposure of sensitive user information [68,81]. Approaches such as cancelable biometrics, one-way transformations, and avoiding the storage of raw biometric data can help protect privacy.…”

Section: Summary Of Challenges With Wearable Technologies Key Generationmentioning

confidence: 99%

See 2 more Smart Citations

The Emerging Challenges of Wearable Biometric Cryptosystems

Ajlan,

Alsboui,

Alshaikh

et al. 2024

Cryptography

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Cryptographic key generation and data encryption and decryption using wearable biometric technologies is an emerging research area with significant potential for authentication and communication security. The research area is rapidly developing, and a comprehensive review of recently published literature is necessary to establish emerging challenges. This research article aims to critically investigate and synthesize current research using biometric cryptosystems that use behavior or medico-chemical characteristics, ranging from gate analysis to gaze tracking. The study will summarize the state of knowledge, identify critical research gaps, and provide insight into promising future implications and applications that can enable the realization of user-specific and resilient solutions for authentication and secure communication demands.

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Section: Discussionmentioning

confidence: 99%

Section: Cryptographic Key Generation Using Biometricsmentioning

confidence: 99%

Section: Summary Of Challenges With Wearable Technologies Key Generationmentioning

confidence: 99%

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The Emerging Challenges of Wearable Biometric Cryptosystems

Ajlan,

Alsboui,

Alshaikh

et al. 2024

Cryptography

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“…The correlation values are used to express the similarity between a test fingerprint and the fingerprints in the database. The higher the score, the higher is the similarity between fingerprints [18][19][20]. Accessing of the system is granted only if the score for a test user is higher than a certain threshold known as the Equal Error Rate (EER), which is estimated at the intersection of the impostor and genuine distributions.…”

Section: 2 2 a Au Ut Th He En Nt Ti Ic Ca At Ti Io On N S St Ta Amentioning

confidence: 99%

Cancelable Fingerprint Recognition based on Encrypted Convolution Kernel in Different Domains

Hashad

Zahran

El-Rabaie

et al. 2020

Menoufia Journal of Electronic Engineering Research

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Peoples' biometrics, such as fingerprints, are unique , as a result it can be used in many evidence security requests, such as employees' registration gate, crime investigation, and revealing smart phones. The security of fingerprints is very critical in protecting the peoples' identity. In this research, a cancelable fingerprint recognition system is proposed. The proposed system is based on comprising four biometrics in a unified biometric template for each person using Discrete Cosine Transform (DCT) compression. This unified biometric template is encrypted with different convolution kernels produced by chaotic Baker map in different domains. The Integer Wavelet Transform (IWT) and the Discrete Wavelet Transform (DWT) are used to create different transformations of the fingerprint. In case a transformed fingerprint is compromised, the biometric fingerprint transformation is reorganized with another transformation. A comparative study between different transform-domains in the occurrence of attacks shows the authority of encryption in the DWT domain with different keys. Both Equal Error Rate (EER) and Area under Receiver Operating Characteristic (AROC) curve are used for performance evaluation revealing high performance of the proposed system.

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“…One of the most advanced alternatives to produce a deformed biometric template is to apply datadependent cryptography. In [32], user fingerprint templates can generate cryptographic keys of an encryption scheme for fingerprints. Therefore, it is sophisticated or impossible for the intruder to impose the secret keys without prior user features.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

et al. 2021

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Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of 2.0243×10 -4 , FAR (False Acceptance Rate) of 4.8843×10 -4 , and FRR (False Rejection Rate) of 2.2693×10 -4 .

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Cryptographic Key Generation Scheme from Cancellable Biometrics

Cited by 10 publications

References 12 publications

The Emerging Challenges of Wearable Biometric Cryptosystems

The Emerging Challenges of Wearable Biometric Cryptosystems

Cancelable Fingerprint Recognition based on Encrypted Convolution Kernel in Different Domains

Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

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