A METHOD FOR CALCULATING CRYPTOGRAPHIC KEYS FROM A PERSON'S BIOMETRIC DATA BASED ON STABLE TRANSFORMATIONS
Abstract
This article discusses the task of converting a person's biometric data into cryptographic keys that provide a high level of security. Biometric data, although unique, does not have sufficient randomness to create strong cryptographic keys. In addition, key storage issues arise: an attacker can steal the template, and the slightest change in the input data (different lighting, facial expressions) creates a risk of inconsistency, which leads to a high frequency of false rejections. As a solution, a cryptographic key generation method is proposed that combines several key technologies to ensure the efficiency and security of the key creation process. The main stages of the method are described, including obtaining a face image, image processing, image analysis with the extraction of necessary features using a convolutional neural network, image transformation (feature vector) into a binary string, and stable transformations. Sustainable transformations are called upon as techniques that are aimed at protecting biometric data: the use of Reed-Solomon correction codes, the generation of a biometrically dependent key, followed by its distribution into parts according to the classical Shamir scheme, encryption. The advantages of this approach have been theoretically justified in the context of reducing the likelihood of false tolerances and false deviations. The results of experiments based on public datasets are presented. It is shown that compared with classical methods simple sampling and some existing schemes (Bio-Hashing without error correction), the proposed solution provides higher accuracy. The presented method provides significant security advantages, making cryptographic systems more suitable for high-security applications
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