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The proposed structure showed that NIST tests were successful and could be used on personal PCs. proposed a TRNG structure using a one-dimensional chaotic map based on mouse movements. showed that two different people would produce different random numbers and that these numbers could be used as biometric signatures. Recently, there have been studies performed on random number generation from human-based noise sources. In addition, postprocessing techniques eliminate potential weaknesses and make TRNG designs strong and flexible. This eliminates the statistical weaknesses of random numbers at the output of the TRNG. To meet the R1 requirement in TRNGs, postprocessing techniques are applied on the random numbers obtained by sampling from noise sources. If the R2 requirement is satisfied, then it is assumed that the R3 and R4 requirements are also satisfied. Because of the unpredictability of random numbers generated by the use of high noise sources with high entropy in TRNGs, it is assumed that the R2 requirement is met. Contrary to PRNGs, there is no need to include extra components in the TRNG system designs for R2, R3, and R4 requirements. TRNGs (True Random Number Generators), which are nondeterministic random number generators, present slower, more expensive, and hardware-dependent solutions compared to PRNGs. Therefore, nondeterministic functions are added to the output functions of PRNGs to guarantee these requirements. PRNGs must meet the requirements specified in Table 1 to be used especially for authentication and key generation.
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The statistical qualities of these numbers produced are close to the ideal. PRNGs (Pseudo Random Number Generators), which are deterministic random number generators, generate numbers with fast, easy, inexpensive, and hardware independent solutions. The number generation in the literature is performed in two different ways as deterministic and nondeterministic. In these applications, particularly numbers should have good statistical properties and be unpredictable and nonreproducible.
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Random numbers are needed in some areas in computer science, such as authentication, secret key generation, game theory, and simulations. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals. The numbers produced from bioelectrical and physical signals were successful in all tests. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. The sampling was achieved by using a nonperiodic and chaotic logistic map. For this purpose, each signal was first normalized and then sampled. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). The signals used in the random number generation were taken from BNCIHORIZON2020 databases.
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This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation.