Properties making a chaotic system a good Pseudo Random Number Generator

We discuss two properties making a deterministic algorithm suitable to generate a pseudo random sequence of numbers: high value of Kolmogorov-Sinai entropy and high-dimensionality. We propose the multi dimensional Anosov symplectic (cat) map as a Pseudo Random Number Generator. We show what chaotic features of this map are useful for generating Pseudo Random Numbers and investigate numerically which of them survive in the discrete version of the map. Testing and comparisons with other generators are performed.Comment: 10 pages, 3 figures, new version, title changed and minor correction

Stock Repurchase Agreements: Close Corporation Use of Designee Provision Permits Repurchase Despite Insufficient Earned Surplus

Statistical random number testing is a well studied field focusing on pseudo-random number generators, that is to say algorithms that produce random-looking sequences of numbers. These generators tend to have certain kinds of flaws, which have been exploited through rigorous testing. Such testing has led to advancements, and today pseudo random number generators are both very high-speed and produce seemingly random numbers. Recent advancements in quantum physics have opened up new doors, where products called quantum random number generators that produce acclaimed true randomness have emerged. Of course, scientists want to test such randomness, and turn to the old tests used for pseudo random number generators to do this. The main question this thesis seeks to answer is if publicly available such tests are good enough to evaluate a quantum random number generator. We also seek to compare sequences from such generators with those produced by state of the art pseudo random number generators, in an attempt to compare their quality. Another potential problem with quantum random number generators is the possibility of them breaking without the user knowing. Such a breakdown could have dire consequences. For example, if such a generator were to control the output of a slot machine, an malfunction could cause the machine to generate double earnings for a player compared to what was planned. Thus, we look at the possibilities to implement live tests to quantum random number generators, and propose such tests. Our study has covered six commonly available tools for random number testing, and we show that in particular one of these stands out in that it has a series of tests that fail our quantum random number generator as not random enough, despite passing an pseudo random number generator. This implies that the quantum random number generator behave differently from the pseudo random number ones, and that we need to think carefully about how we test, what we expect from an random sequence and what we want to use it for.Statistisk slumptalstestning är ett väl studerat ämne som fokuserar på så kallade pseudoslumpgeneatorer, det vill säga algorithmer som producerar slump-liknande sekvenser med tal. Sådana generatorer tenderar att ha vissa defekter, som har exploaterats genom rigorös tesning. Sådan testning har lett till framsteg och idag är pseudoslumpgeneratorer både otroligt snabba och producerar till synes slumpade tal. Framsteg inom kvantfysiken har lett till utvecklingen av kvantslumpgeneratorer, som producerar vad som hävdas vara äkta slump. Självklart vill forskare utvärdera sådan slump, och har då vänt sig till de gamla testerna som utvecklats för pseudoslumpgeneratorer. Den här uppsatsen söker utvärdera hurvida allmänt tillgängliga slumptester är nog bra för att utvärdera kvantslumpgeneratorer. Vi jämför även kvantslumpsekvenser med pseudoslumpsekvenser för att se om dessa väsentligen skiljer sig från varandra och på vilket sätt. Ett annat potentiellt problem med kvantslumpgeneratorer är möjligheten att dessa går sönder under drift. Om till exempel en kvantslumpgenerator används för att slumpgenerera resultatet hos en enarmad bandit kan ett fel göra så att maskinen ger dubbel vinst för en spelare jämfört med planerat. Därmed ser vi över möjligheten att implementera live-tester i kvantslumpgeneratorer, och föreslår några sådana tester. Vår studie har täckt sex allmänt tillgängliga verktyg för slumptalstestning, och vi visar att i synnerhet ett av dessa står ut på så sätt att det har en serie av tester som slumptalen från vår kvantslumpgenerator inte anser är nog slumpade. Trots det visar samma test att sekvensen från pseudoslumpgeneratorerna är bra nog. Detta antyder att kvantslumpgeneratorn beter sig annorlunda mot pseudoslumpgeneratorerna, och att vi behöver tänka över ordentligt kring hur vi testar slumpgeneratorer, vad vi förväntar oss att få ut och hurvida detta påverkar det vi skall använda slumpgeneratorn till

Improving random number generators by chaotic iterations. Application in data hiding

In this paper, a new pseudo-random number generator (PRNG) based on chaotic iterations is proposed. This method also combines the digits of two XORshifts PRNGs. The statistical properties of this new generator are improved: the generated sequences can pass all the DieHARD statistical test suite. In addition, this generator behaves chaotically, as defined by Devaney. This makes our generator suitable for cryptographic applications. An illustration in the field of data hiding is presented and the robustness of the obtained data hiding algorithm against attacks is evaluated.Comment: 6 pages, 8 figures, In ICCASM 2010, Int. Conf. on Computer Application and System Modeling, Taiyuan, China, pages ***--***, October 201

Pseudo-random number generator for the Sigma 5 computer

A technique is presented for developing a pseudo-random number generator based on the linear congruential form. The two numbers used for the generator are a prime number and a corresponding primitive root, where the prime is the largest prime number that can be accurately represented on a particular computer. The primitive root is selected by applying Marsaglia's lattice test. The technique presented was applied to write a random number program for the Sigma 5 computer. The new program, named S:RANDOM1, is judged to be superior to the older program named S:RANDOM. For applications requiring several independent random number generators, a table is included showing several acceptable primitive roots. The technique and programs described can be applied to any computer having word length different from that of the Sigma 5

A novel pseudo-random number generator based on discrete chaotic iterations

Security of information transmitted through the Internet, against passive or active attacks is an international concern. The use of a chaos-based pseudo-random bit sequence to make it unrecognizable by an intruder, is a field of research in full expansion. This mask of useful information by modulation or encryption is a fundamental part of the TLS Internet exchange protocol. In this paper, a new method using discrete chaotic iterations to generate pseudo-random numbers is presented. This pseudo-random number generator has successfully passed the NIST statistical test suite (NIST SP800-22). Security analysis shows its good characteristics. The application for secure image transmission through the Internet is proposed at the end of the paper.Comment: The First International Conference on Evolving Internet:Internet 2009 pp.71--76 http://dx.doi.org/10.1109/INTERNET.2009.1

A Pseudo Random Numbers Generator Based on Chaotic Iterations. Application to Watermarking

In this paper, a new chaotic pseudo-random number generator (PRNG) is proposed. It combines the well-known ISAAC and XORshift generators with chaotic iterations. This PRNG possesses important properties of topological chaos and can successfully pass NIST and TestU01 batteries of tests. This makes our generator suitable for information security applications like cryptography. As an illustrative example, an application in the field of watermarking is presented.Comment: 11 pages, 7 figures, In WISM 2010, Int. Conf. on Web Information Systems and Mining, volume 6318 of LNCS, Sanya, China, pages 202--211, October 201

Pseudo-Random Number Generators for Vector Processors and Multicore Processors

Large scale Monte Carlo applications need a good pseudo-random number generator capable of utilizing both the vector processing capabilities and multiprocessing capabilities of modern computers in order to get the maximum performance. The requirements for such a generator are discussed. New ways of avoiding overlapping subsequences by combining two generators are proposed. Some fundamental philosophical problems in proving independence of random streams are discussed. Remedies for hitherto ignored quantization errors are offered. An open source C++ implementation is provided for a generator that meets these needs

A novel pseudo-random generator based on discrete chaotic iterations

International audienceSecurity of information transmitted through the Internet, against passive or active attacks is an international concern. The use of a chaos-based pseudo-random bit sequence to make it unrecognizable by an intruder, is a field of research in full expansion. This mask of useful information by modulation or encryption is a fundamental part of the TLS Internet exchange protocol. In this paper, a new method using discrete chaotic iterations to generate pseudo-random numbers is presented. This pseudo-random number generator has successfully passed the NIST statistical test suite (NIST SP800-22). Security analysis shows its good characteristics. The application for secure image transmission through the Internet is proposed at the end of the paper