ĭe Bernardi, M., Khouzani, M.H.R., Malacaria, P.: Pseudo-random number generation using generative adversarial networks. IEEE (2009)ĭe la Fraga, L.G., Torres-Pérez, E., Tlelo-Cuautle, E., Mancillas-López, C.: Hardware implementation of pseudo-random number generators based on chaotic maps. In: 2009 Joint IEEE North-East Workshop on Circuits and Systems and TAISA Conference, pp. 52(2), 551–561 (2013)Īzzaz, M., Tanougast, C., Sadoudi, S., Dandache, A.: Real-time FPGA implementation of Lorenz’s chaotic generator for ciphering telecommunications. Pande, A., Zambreno, J.: A chaotic encryption scheme for real-time embedded systems: design and implementation. In: Proceedings of the 19th International Conference Mixed Design of Integrated Circuits and Systems, MIXDES 2012, pp. Keywordsĭabal, P., Pelka, R.: FPGA implementation of chaotic pseudo-random bit generators. The result shows that the proposed GAN-based PRNG achieved high randomness even on embedded processors. Compared with the previous method, the proposed method reduced the percentage of test failures by 2.85x. Finally, generated random numbers were tested through the NIST random number test suite. To the best of our knowledge, this is the first GAN based PRNG for embedded processors. Also, our PRNG achieved a speed of 1.0 GB/s, which is about 6.25x compared to the speed of other lightweight PRNG. The PRNG generates random numbers in 13.27 ms using the Edge TPU. During model training, the number of epochs is significantly reduced with the proposed approach. To support the Edge TPU, the proposed GAN based PRNG is converted to a TensorFlow Lite model. The proposed method is also efficiently implemented on embedded processors by using the Edge TPU. The proposed design generates a random number of 1,099,200-bits with a 64-bit seed. A recurrent neural network (RNN) layer is used to overcome the problems of predictability and reproducibility for long random sequences, which is found in the result of the NIST test suite for the previous method. In this paper, we present a novel PRNG based on generative adversarial networks (GAN). The axiomatic unpredictability definition and the cryptographically secure definition will not hold for anything other than a uniform distribution.A pseudo-random number generator (PRNG) is a fundamental building block for modern cryptographic solutions. In all three cases, it is fine to use the word “random” and people will know what you mean from the context, but when it comes to making inferences from the underlying definitions, it is important to be clear. In Cryptography it is defined in terms of an adversary not being able to distinguish output of the RNG from what they would get from a truly uniform distribution. In Cryptography, we are back to unpredictability, but it is defined slightly differently. (Indeed, estimating distributions and parameters from a sample is a huge part of Statistics.) Underlyingly, Statistics uses definitions based on measure theory so that they work over real numbers. In Statistics you can talk about random variables of different distributions. The definition you quote is the “axiomatic unpredictability” one from Information Theory. I think that the question is the result of the collision and overlap of three different areas of mathematics. Even if they aren’t told up front that the distribution is normal, after enough input they will be able to compute good estimates of the distribution and its parameters. In your example of a normal distribution the adversary gets that advantage by picking values near the mode. If you have a non-uniform distribution the adversary will have an advantage when guessing. If your reading materials are leaving you with questions like these, maybe have a look at something more rigorous. Let $\ell$ be a polynomial and let $G$ be a deterministic polynomial-time algorithms such that for any $n$ and any input $ \in \$ For example in Katz & Lindell's textbook (2nd edition), Definition 3.14 (p. And in fact you should be able to conclude that your definition and interpretation implies that the output of a secure PRNG must be uniform.īut the definitions used in theoretical cryptography are much more precise than this. If we follow your logic strictly, then we have to conclude that your proposed attack on the normally-distributed output of $G$ would imply that $G$ is not in fact a secure PRNG by your definition and application thereof. You'd do well to review your readings to see if they stipulate somewhere earlier that when they say "random" they mean uniform random (equiprobable).īut even without such a stipulation that I'd say the definition as you've loosely formulated it and are interpreting it seems to imply equiprobability.
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