![]() ![]() #RANDOM SEMBLANCE GENERATOR GENERATOR#In cryptography, there is no simple methodology to obtain a True Random Number Generator (TRNG) that meets statistical requirements such as unpredictability, uniform distribution (bias) and independence (correlation) for random numbers. Results indicate that the proposed chaos-based TRNG is fast, evenly distributed, and is secure enough for applications that have high security requirements. We also perform an in-depth entropy analysis of the generator’s outputs and measure its degree of non-periodicity. Next, the proposed generator is scrutinized based on a standardized set of evaluation criteria which includes the use of multiple statistical test suites followed by an analysis of its non-deterministic property. We first perform experiments to depict the unpredictable nature of thread access due to race conditions through entropy and scale index analysis. These networks are formulated by coupling chaotic maps in the form of chaotic coupled map lattices which have the capability to amplify minor uncertainties, leading to better performance as compared to other CPU-based TRNGs. The novelty of this work lies in its use of chaotic networks capable of extracting entropy while postprocessing outputs simultaneously. Although prior work using the same entropy source exists, they either have low efficiency or insufficient security analysis. The entropy source is the unpredictable sequence of thread access when parallel threads attempt to access the same memory location, known as race condition or data races. The effectiveness of the proposed post-processing method.Ī true random number generator (TRNG) is proposed, harvesting entropy from multicore CPUs to generate non-deterministic outputs. The proposed generators are able to produce secure true random sequences at a high throughput, which in turn reflects on Generators are analyzed to identify statistical defects in addition to forward and backward security. It is applied in designing TRNGs based on digital audio. To depict the feasibility of the proposed post-processing algorithm, Is then iterated to produce a set of random output bits. Used to perturb the parameters of a hyperchaotic map, which Quantized bits of a physical entropy source are The proposed method utilizes the inherent characteristics of chaos such as hypersensitivity to input changes, diffusion, and confusion capabilities to achieve Post-processing method based on hyperchaos is proposed for software-based TRNGs which not only eliminates statistical biases but also provides amplification in order to improve the performance of TRNGs. These generators usually require post-processing algorithms to eliminate biases but in turn, reduces performance. However, most TRNGs require specialized hardware to extract entropy from physical phenomena and tend to be slower than PRNGs. ![]() They produce unpredictable, non-repeatable random sequences. True random number generators (TRNG) are important counterparts to pseudorandom number generators (PRNG), especially for high security applications such asĬryptography. ![]()
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