Biometric Cryptosystems: Overview, State-of-the-Art and Perspective Directions

Lutsenko, Maria; Кuznetsov, Alexandr; Кіян, Анастасія; Смірнов, Олексій; Кузнецова, Тетяна

doi:10.1007/978-3-030-58359-0_5

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…In comparison to other approaches to key generation and binding, cryptographic key binding is an industry leader. Its reliance on biometric characteristics provides a higher level of security than conventional methods that rely on passwords [102]. Its unique advantages should be carefully considered when evaluating its suitability for various contexts.…”

Section: Cryptographic Key Binding Techniquesmentioning

confidence: 99%

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: Cryptographic Key Binding Techniquesmentioning

confidence: 99%

The Emerging Challenges of Wearable Biometric Cryptosystems

Ajlan,

Alsboui,

Alshaikh

et al. 2024

Cryptography

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“…Біометрія традиційно вважається важливою сферою сучасної кібербезпеки [1 -3]. Наприклад, біометричні методи автентифікації широко використовуються в різних додатках: криміналістика, електронна комерція, захист авторських прав, електронний документообіг, системи контролю доступу та багато іншого [1,4,5].…”

Section: вступunclassified

“…Традиційно нечіткі екстрактори, як і попередні їм нечіткі контейнери [4], будуються з використанням методів кодування з виправленням помилок. На початковому етапі біометричні дані в певному сенсі «зливаються» з елементами кодів, що виправляють помилки (наприклад, з кодовими словами або синдромальними послідовностями).…”

Section: вступunclassified

Deep learning-based models’ application to generating a cryptographic key from a face image

Kuznetsov,

Zakharov

2023

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Generating cryptographic keys, such as passwords or pin codes, involves utilizing specialized algorithms that rely on complex mathematical transformations. These keys necessitate secure storage measures and complex distribution and processing mechanisms, which often incur substantial costs. However, an alternative approach emerges, proposing the generation of cryptographic keys based on the user's biometric data. Since one can generate keys "on the fly," there is no longer a requirement for key storage or distribution. These generated keys, derived from biometric information, can be effectively employed for biometric authentication, offering numerous advantages. Additionally, this alternative approach unlocks new possibilities for constructing information infrastructure. By utilizing biometric keys, the initiation of cryptographic algorithms like encryption and digital signatures becomes more streamlined and less burdensome in storing and processing procedures. This paper explores biometric key generation technologies, focusing on applying deep learning models. In particular, we employ convolutional neural networks to extract significant biometric features from human face images as the foundation for subsequent key generation processes. A comprehensive analysis involves extensive experimentation with various deep-learning models. We achieve remarkable results by optimizing the algorithm's parameters, with the False Reject Rate (FRR) and False Acceptance Rate (FAR) approximately equal and less than 10%. With code-based cryptographic extractors’ post-quantum level of security, we ensure the continued protection and integrity of sensitive information within the cryptographic framework.

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