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A METHOD FOR CALCULATING CRYPTOGRAPHIC KEYS FROM A PERSON'S BIOMETRIC DATA BASED ON STABLE TRANSFORMATIONS
I.V. Kaliberda36-522025-11-10Abstract ▼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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DIGITAL MULTIPLIER-CONVERTING METHOD FOR MEASURING FREQUENCY INSTABILITY USING THE LABVIEW PROGRAMMING ENVIRONMENT
Jacinto Mba Biye Nsue, V. P. Fedosov , S. V. Kucheryavenko2020-10-11Abstract ▼The article is aimed at measuring the parameters of the harmonic process by the multiplication-
conversion method. The simulation was carried out through the use of the LabVIEW software
environment, as applied to the digital multiplier-conversion method, the main points of which are
presented in the form of a progressive chain: a) development of the first harmonic process; b) the
multiplication of the indicator of the first harmonic process by four; c) the arrival of to
the band-pass filter PF1 tuned to the highest frequency, in this case, d) simultaneously, using
the generator Г2, a second source signal is generated; e) This oscillation is raised to the
fifth power, f) using the filter PF2 tuned to a frequency of 5 , select the fifth harmonic g) The
signals received at the outputs of the filters are added and the result of the sum is subjected to nonlinear
transformation h) Then, from the resulting square of the sum of the signals and using a
band-pass filter PF3, we extract only the low-frequency harmonic with the frequency i) Then,
using the Hilbert transform, we extract the total instantaneous phase from the harmonic and it
becomes the object of the derivative operation, which leads us to obtain the instantaneous frequency
function, characterized by a fixed dispersion. j) The law of fluctuations of the frequency
resulting from the use of multiplication-conversion operations is compared with a given frequency,
and we proceed to determine the mathematical expectation and standard deviation. The conclusion
about the frequency instability is based on the discrepancies obtained. Applying nonlinear
transformations of oscillations of oscillators similar in instability and obtaining the oscillations of
a given frequency in the same way, the measured frequency instability is established. If you apply
this method many times to the oscillations of highly stable devices, you can develop an oscillation
with increased instability, and then evaluate it with available measuring equipment. Thus, we bypass
without high costs by performing this operation. Then, determine the initial instability by the
formulas given in this article. -
SELECTION OF THE SENSOR CONVERSION CHARACTERISTIC MODEL FOR CONTROLLING THE ERROR IN THE MEASUREMENT OF PHYSICAL QUANTITIES
S.I. Klevtsov2022-08-09Abstract ▼On the example of a pressure sensor, the problem of selecting a model and parameters of
the conversion function of a microprocessor sensor is considered. The conversion function is
based on a mathematical model that associates the electrical signal coming from the sensor's
measuring transducer with the value of a physical quantity. The model of the conversion function
of a microprocessor sensor must repeat the real spatial dependence of the electrical signal on the
measured value and take into account the influence of external factors, such as temperature. Microprocessor
sensors are used to measure the parameters of an object with a given accuracy. The
main contribution to the measurement error is made by the inaccuracy of the approximation of the
real transformation function by its model. The need to achieve the optimal level of parameter
measurement error in the system, taking into account the complexity and cost of measurements,
requires the control of the sensor error. For this purpose, various models and methods of approximation
are presented. For efficient error control, a method of multi-segment spatial approximation
based on models of linear or non-linear spatial elements is proposed. The error control procedure
is formulated. The procedure for using the model of multi-segment spatial approximation
of the transformation characteristic for pressure calculations taking into account the influence of
temperature is based on the combined use of linear and non-linear spatial elements within the
same model. The segment type selection procedure should begin with an assessment of the possibility
of using a linear spatial element first, and if it is impossible to meet the accuracy requirements,
an analysis of the use of a non-linear element. The method allows you to change the types
and configuration of spatial elements and in this way influence the measurement error. The advantages
of this approach are confirmed by the simulation results.








