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The main objective of the research is the analysis of the practical applicability of the cryptographically secure software Blum-Blum-Shub pseudo-random number generator for different authentication and encryption tasks. It is shown that the considered pseudo-random sequence generator, which has a high computational complexity, can be effectively used in those cryptographic tasks, which require low key generation rate. An alternative way of implementing the Password-Based Key Derivation Function, which is based on the use of the Blum-Blum-Shub generator as a pseudo-random function, is proposed. The proposed algorithm allows to slow down dictionary and brute-force attacks. Experimental studies have shown that the developed algorithm allows to adaptively adjust the guaranteed minimum time of generating a cryptographic key for specific tasks of authentication and encryption.

This paper deals with the security of chaos-based "true" random number generators (RNG)s. An attack method is proposed to analyze the security weaknesses of chaos-based RNGs and its convergence is proved using a master slave synchronization scheme. Attack on a RNG based on a double-scroll attractor is also presented as an example. All secret parameters of the RNG are revealed where the only information available is the structure of the RNG and a scalar time series observed from the double-scroll attractor. Simulation and numerical results of the proposed attack method are given such that the RNG doesn't fulfill NIST-800-22 statistical test suite, not only the next bit but also the same output bit stream of the RNG can be reproduced.

Today, cryptographic security depends primarily on having strong keys and keeping them secret. The keys should be produced by a reliable and robust to external manipulations generators of random numbers. To hamper different attacks, the generators should be implemented in the same chip as a cryptographic system using random numbers. It forces a designer to create a random number generator purely digitally. Unfortunately, the obtained sequences are biased and do not pass many statistical tests. Therefore an output of the random number generator has to be subjected to a transformation called post-processing. In this paper the hash function SHA-256 as post-processing of bits produced by a combined random bit generator using jitter observed in ring oscillators (ROs) is proposed. All components – the random number generator and the SHA-256, are implemented in a single Field Programmable Gate Array (FPGA). We expect that the proposed solution, implemented in the same FPGA together with a cryptographic system, is more attack-resistant owing to many sources of randomness with significantly different nominal frequencies.

© 2017 Association for Computing Machinery.This research aims to design a hardware random number generator running on wireless identification and sensing platform (WISP), which is a lightweight Internet of things device. The accelerometer sensor on WISP is used as the entropy source. This entropy source is post-processed with de-biasing and extraction methods to provide more uniformly distributed results that can be used in the authentication protocols between a radio frequency identification (RFID) tag and an RFID reader. The obtained random number outputs are tested using the well-known NIST random number test suite. It is seen that the numbers pass all the tests in the NIST randomness test suite.

Random numbers have a wide range of usage area such as simulation, games of chance, sampling and computer science (cryptography, game programming, data transmission). In order to use random numbers in computer science, they must have three basic requirements. First, the numbers generated must be unpredictable. Second, the numbers generated should have good statistical properties. Finally, the generated number streams must not be reproduced. Random number generators (RNGs) have been developed to obtain random numbers with these properties. These random number generators are classified into true random number generators (TRNG) and pseudo random number generators (PRNG). One of the PRNGs used for generate random numbers is Stream Encryption algorithms. In this paper, random number generation of LFSR based stream encryption algorithms and their hardware implementations are presented. LFSR based stream encryption algorithms have been implemented on Altera's FPGA based 60-nm EP4CE115F29C7 development boards by using VHDL language. The obtained random numbers passed the NIST statistical tests, accepted as standard for cryptographic applications.

This paper shows the research on hardware random number generator (HRNG). As truly random numbers are strongly required in encryption and computer simulation areas, developing a simple and inexpensive HRNG has significant value. The whole system is divided into the noise generating module and the processing module. After the numbers are generated, a randomness test has been carried out which indicates that the random numbers generated are truly random. It is concluded that the final product of this HRNG meets the requirements of the objectives.

Real random values have wide range of application in different field of computer science such as cryptography, network security and communication, computer simulation, statistical sampling, etc. In purpose of generating real random values, need for a natural noisy source refers to the main challenge where a source of noise may be reliable for using in random number generator if and only if be derived from physical environment. In this work, we address this requirement by using DNA computing concepts where the molecular motion behavior of DNA molecular provides a pure source of physical noise that may be used for generating high quality real random values. Since one of the main factor for evaluating quality of real random values refer to expectation for generating approximately same amount of 0s and 1s, in this article we model a DNA-based random number generator in sticker mode with ability of generating equal numbers of 0 and 1. After using molecular motion behavior of DNA molecular as the natural source of noise into the proposed DNA-based random number generator, the generated value were subjected to frequency, run, and serial tests which are proposed by National Institute of Standards and Technology (NIST) for randomness evaluation. Obtained result from this evaluation shows that beside the achieving high scores in run and serial tests, the values generated by our DNA-based random number generator pass frequency test with 100% success.

Random number generators are an important tool for cryptographic applications. In cryptographic protocol, randomness is essential properties since inadequate source of randomness can be effect security of whole system. This paper describes requirements of a robust random generator and proposes hybrid architecture to realize these requirements. Security analysis shows that output of proposed generator looks random. Therefore, proposed generator is used for cryptographic solutions.

Linear Feedback Shift Register based Unique Random Number Generator is an enhancement of Random Number generator with the additional property that any number generated by a unique random number generator can’t be duplicated. As per users demand for not duplicated random numbers in some applications like transferring a random number over the network on the behalf of actual character of the message for security point of view, existence of unique random number generators are very essential. In this paper LFSR [1] (Linear Feedback Shift Register) is used to implement the proposed concept of unique random number generator. Using LFSR is a faster approach for generating random sequences because it requires only X-OR operations and shift registers that’s why its implementation is very easy in software as well as in hardware [2, 3]. We can easily modify the LFSR and produce different random sequences. So it is the best option for using LFSR in unique random number generator.

SUMMARY This paper presents an oscillator-based true random number generator (TRNG) that dynamically unbiases 0/1 probability. The proposed TRNG automatically adjusts the duty cycle of a fast oscillator to 50%, and generates unbiased random numbers tolerating process variation and dynamic temperature fluctuation. A prototype chip of the proposed TRNG was fabricated with a 65 nm CMOS process. Measurement results show that the developed duty cycle monitor obtained the probability of ‘1’ 4,100 times faster than the conventional output bit observation, or estimated the probability with 70 times higher accuracy. The proposed TRNG adjusted the probability of ‘1’ to within 50 ± 0.07% in five chips in the temperature range of 0◦ Ct o 75 ◦C. Consequently, the proposed TRNG passed the NIST and DIEHARD tests at 7.5 Mbps with 6,670µm 2 area.

The success of any cryptographic algorithm lies in the unpredictability& irreproducibility of random number generation. For implementing the cryptographic algorithms it should be integrated within a single chip. Hence a random number generator which produces unpredictable random numbers as well as occupying less area is the essential one. In this work, random numbers are generated based on sampling two oscillator frequencies. Jitters and phase noise's usually considered as disturbance signals. Here the time when they appear in the oscillators is the main thing to generate random numbers. However, when these algorithms are implemented in smart cards they have to produce the same result throughout its life. But actually there are some issues to reproduce the same number due to aging. In this paper, an efficient method is proposed to mitigate the effects of aging in TRNGs. Here, a tetrahedral oscillator-based true random number generator (TRNG) is presented and the issues about aging which affect the random key generated by the TRNG are addressed. So we proposed an aging resistant tetrahedral oscillator concept to mitigate the impact of aging in the key generator. The performance of the proposed random number generator is measured by statistical tests. This TRNG is used as a private key generator for cryptographic algorithm and its operation is programmed and the simulations have been done by ModelSim-Altera6.4a (Quartus-II 9.0) starter Edition". To prove the proposed tetrahedral aging-resistant oscillator operation it is designed in TANNER EDA tool v14.1 (S-Edit Schematic Editor). By varying the supply voltage and transistor channel width and length, it is visible that the effects of run-time stress is mitigated which affect the lifetime of the oscillator in TRNG.

Hardware information security has become an important structure and application metric. The existing hardware security has a well designed security measures but it only address a certain amount of rising security requirements and is slow for a lot of the emerging security primitives Physical true random number generators (TRNGs) appear to be critical components of many cryptographic systems. Random number generators are used to combat this problem by producing different and unique identification for each user in a network. This paper analyzes on how memristors can be implemented in ring oscillator (RO) based random number generators and how the random number generator measure randomness and its value. The proposed random number generator produces a randomness value of 25.2µ.

True random number generators (TRNG) play an important role in many fields that require unpredictable and nondeterministic random number sequences. Unlike their pseudorandom counterparts, TRNGs are more computationally expensive as they need to harvest entropy from physical phenomena. To generate high quality true random numbers at a fast rate, this paper introduces a new TRNG based on hyperchaos and digital sound. The characteristics of a hyperchaotic map such as sensitivity to initial conditions and complex behavior amplifies noise obtained by sampling environmental sound through a computer microphone. The random numbers generated are then evaluated using statistical test suites such as NIST SP 800-22, DIEHARD and ENT. Because nondeterminism cannot be proved by merely running test suites, entropy analysis is performed to determine the unpredictability of these sequences. Results show that the proposed TRNG can generate true random numbers at a high rate while maintaining strong statistical quality. In addition, the entropy source requires only an inexpensive computer microphone which is already built into many laptops and handheld devices. Therefore, the proposed generator provides a low-cost and easily obtainable option for applications that require true random numbers.

A good Random Number Generator has to be required for different applications, the produce method is depended on the concrete application device. This paper provided a random number generator based on a general purpose MCU. By means of its ADC module and few components, a good true random number can be achieved.

A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. This paper presents the entropy and p-value tests performed on classical and quantum random number generators, in order to check the randomness of the generated output data. Both generators have been integrated in software applications reproducing asymmetrical cryptosystems, their results contributing to the generation of key material within Diffie-Hellman protocol. Can regard these tests as a first step in determining if a generator is suitable for a particular cryptographic application. At the same time, the statistical tests cannot serve as a substitute for cryptanalysis.

Nowadays information technologies are widespread and used in every computer-based system, hence information security tasks are quite important and their successful solution is required in business process. Cryptographic means are used in different applications, especially in cases when data confidentiality should be provided; although they can be utilized to maintain data availability and integrity, user's anonymity, author's non-repudiation and so on. Many information security tools use random numbers, but unfortunately, quality of output random numbers and speed of their generation do not satisfy modern requirements. The rate of production-run random number generators is limited by the physical processes used. One of the reasons of low random numbers generating rate is application of binary events. Many generators use analog events, e.g. noise in electronic devices, converted to binary numbers utilizing the threshold value or quantum discrete events, e.g. photon passing through the polarizer. The main idea of this work is that one can increase random number generator's rate using the non-binary sequences, e.g. non-binary quantum processes.

True random number generators, which output truly random numbers by extracting entropy from physical phenomena such as thermal and electronic noises, are widely used in the field of the cryptographic communication systems. We have been developing a superconductive true random number generator that can generate truly random number sequences, impossible to be predicted, by utilizing the high-speed operation and high-sensitivity of superconductive integrated circuits. In this study, we have calculated the dependences of correlation of output random bits on the generation rate. Statistical tests have been performed on the basis of the NIST statistical test suite in order to evaluate the quality of the randomness of sequences generated by the superconductive true random number generator at high generation rate. We have generated a random number sequence consisting of 3.2 M-bit at the generation rate of 30 Gbit/s using the superconductive true random number generator, fabricated by the ISTEC-SRL 2.5 kA/cm2 Nb standard process. The generated random number sequences passed 13 kinds of the statistical tests in the NIST statistical test suit, although the 3 tests were not performed because of the shortage of the generated random numbers. The result sufficiently proves that a superconductive true random number generator can generate a high quality of random numbers that can be used for practical cryptographic applications, at a generation rate of up to 30 Gbit/s.

The PseudoRandom Number Generator (PRNG) used by the Windows operating system is the most commonly used PRNG. The pseudorandomness of the output of this generator is crucial for the security of almost any application running in Windows. Nevertheless, its exact algorithm was never published. We examined the binary code of a distribution of Windows 2000. This investigation was done without any help from Microsoft. We reconstructed the algorithm used by the pseudorandom number generator (namely, the function CryptGenRandom). We analyzed the security of the algorithm and found a nontrivial attack: Given the internal state of the generator, the previous state can be computed in 2 23 steps. This attack on forward security demonstrates that the design of the generator is flawed, since it is well known how to prevent such attacks. After our analysis was published, Microsoft acknowledged that Windows XP is vulnerable to the same attack. We also analyzed the way in which the generator is used by the operating system and found that it amplifies the effect of the attack: The generator is run in user mode rather than in kernel mode; therefore, it is easy to access its state even without administrator privileges. The initial values of part of the state of the generator are not set explicitly, but rather are defined by whatever values are present on the stack when the generator is called. Furthermore, each process runs a different copy of the generator, and the state of the generator is refreshed with system-generated entropy only after generating 128KB of output for the process running it. The result of combining this observation with our attack is that learning a single state may reveal 128KB of the past and future output of the generator. The implication of these findings is that a buffer overflow attack or a similar attack can be used to learn a single state of the generator, which can then be used to predict all random values, such as SSL keys, used by a process in all its past and future operations. This attack is more severe and more efficient than known attacks in which an attacker can only learn SSL keys if it is controlling the attacked machine at the time the keys are used.

True random number generators are in general more secure than pseudo random number generators. In this paper, we propose a novel true random number generator which generates a 256-bit random number by computer mouse movement. It is cheap, convenient and universal for personal computers. To eliminate the effect of similar movement patterns generated by the same user, three chaos-based approaches, namely, discretized 2D chaotic map permutation, spatiotemporal chaos and “MASK” algorithm, are adopted to post-process the captured mouse movements. Random bits generated by three users are tested using NIST statistical tests. Both the spatiotemporal chaos approach and the “MASK” algorithm pass the tests successfully. However, the latter has a better performance in terms of efficiency and effectiveness and so is more practical for common personal computer applications.

We describe how dependencies between random numbers generated with some popular pseudo-random number generators can be detected using general purpose machine-learning techniques. This is a novel approach, since usually pseudo-random number generators are evaluated using tests specifically designed for this purpose. Such specific tests are more sensitive. Hence, detecting the dependence using machine-learning methods implies that the dependence is indeed very strong. The most important example of a generator, where dependencies may easily be found using our approach, is MATLAB's function rand if the method state is used. This method was the default in MATLAB versions between 5 (1995) and 7.3 (2006b), i.e., for more than 10 years. In order to evaluate the strength of the dependence in it, we used the same machine-learning tools to detect dependencies in some other random number generators, which are known to be bad or insufficient for large simulations: the infamous RANDU, ANSIC, the oldest generator in C library, minimal standard generator, suggested by Park and Miller (1988), and the rand function in Microsoft C compiler.

Background: Pseudo random numbers have indispensable role in designing cryptography systems such as key stream in stream ciphers. Efficiency of most crypto systems are in depend on the quality of secret key generated by a pseudo random number generator. Improvements/Methods: In the present paper, an efficient pseudo random number generator is presented for cryptographic applications. The algorithm is based on controlling distribution of generated random numbers with the chaotic henon congruential generator. Statistical Analyses: Statistical tests and histograms are performed over the proposed generator and the results confirm the improvements over the proposed algorithms. According to the results of statistical tests, proposed algorithms generate pseudo random numbers with acceptable independency and uniformity and random sequences with long enough period. Applications: Key streams in stream cipher system can be considered as the most important applications of pseudo random numbers. With this regard proposed generators are statistically proved as proper key stream generator for designing stream cipher systems.

International audience; Generating random numbers is essential in manycryptographic applications like key generation, cryptographicprotocols for example Transport Layer Security (TLS) protocol,nonce and also in Internet for choosing TCP sequence numbers.We need generators which are able to construct large amountsof secure random numbers. To this end, True Random NumberGenerators (TRNGs) which extract randomness from physicalprocesses are usually used. The sequences generated by TRNGscannot be reproduced. However, generating random numbersby this way is time-consuming and expensive. Another way togenerate random numbers is to use deterministic random numbergenerators in which the seed is reseeded many times during thegeneration of the sequence. In this paper, we propose a newpseudo-chaotic number generator (PCNG) that produces randomnumbers. The algorithm is refreshed many times by using entropysource from Linux kernel. Results of statistical properties (i.e.Nist test, auto and cross-correlation, histogram, chi2-test andsoftware security analysis) exhibit good performance thus demonstratingthat the proposed generator can be used confidently toproduce random numbers.

The importance of random number generators has increased over the years. This follows from the fact that contemporary research methods rely more and more on simulation and the increased importance of encryption technology. The output of a random number generator is created by either an algorithm or a physical device. The most popular method for random number generation is through the use of an algorithm. This article presents a new category of physical random bit generator that is packaged by several manufacturers. A statistical analysis of the output from the generators is given.

Random numbers generators are widely used in different fields like cryptography, gaming development, artificial intelligence, etc. Being the engine of some security (cryptographic) protocols, the development of good and secure Random Number Generators aroused the attention of the research communities worldwide. Random Number Generators split into two categories: True Random Number Generators (based on physical non-deterministic processes, like the jitter of an oscillator) and Pseudo Random Number Generators (based on mathematical properties, where the output at the time t is based somehow on the output on time t-1). It is well known that the output distribution of a Random Number Generator (either True or Pseudo) output tends toward 50%. There are some particular cases where this probability must be different (some of these situations will be described here) and this paper will present a way of obtaining this. The novelty of this paper is based on a new approach on the classic Gollmann Cascade PRNG and starting from this, the development of a FPGA implementation to increase its security.

Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks.

Different security tools are needed for the security of mobile devices. Random number generators are an important tool for security applications. In this paper, a robust true random number generator algorithm has been proposed for mobile devices. An application of the algorithm is shown in two-level authentication application. Security analysis shows that proposed algorithm has good performance characteristics.

Pseudo Random Number Generator (PRNG) uses a deterministic system and an initial seed to generate random numbers. In order for the output sequence to be truly random, a truly random input seed is used. Most True Random Number Generators (TRNG), use noise in the form nuclear decay, atmospheric noise, electrical noise or Brownian motion as their initial seed. In order to reduce the computational complexity, we use a simple setup of a fish tank as the variable environment, capturing its images over time. The image data is then applied to a reduction algorithm and hash function to generate the initial seed. We propose a cost efficient method of extracting the true seed from the image data and applying it to a pseudo random generator, a Linear Congruential Generator (LCG) in our case to give true random numbers.

A one-way coupled chaotic map lattice is used for generating pseudo-random numbers. It is shown that with suitable cooperative applications of both chaotic and conventional approaches, the output of the spatiotemporally chaotic system can easily meet the practical requirements of random numbers, i.e., excellent random statistical properties, long periodicity of computer realizations, and fast speed of random number generations. This pseudo-random number generator system can be used as ideal synchronous and self-synchronizing stream cipher systems for secure communications.

A random number generator based on the addition of points on an elliptic curve over finite fields is proposed. By using the proposed generator together with the elliptic curve cryptography (ECC) algorithm, we can save hardware and software components. For hardware implementation, the proposed generator can be implemented using the existing ECC arithmetic processor. Up to 29% of gate counts can be saved when compared to the case of implementing a random number generator separately. Theoretical analyses show that periods of the proposed random number generator are sufficiently long. Moreover, the generated sequences have passed the FIPS 140-2 statistical tests. As a result, the proposed generator is suitable to be a reliable and efficient random number generator in ECC systems.

Random numbers with high cryptographic quality are needed to enhance the security of cryptography applications. Software heuristics for generating empirically strong random number sequences rely on entropy gathering by measuring unpredictable external events. These generators only deliver a few bits per event. This limits them to being used as seeds for pseudorandom generators.General-purpose processors feature a large number of hardware mechanisms that aim to improve performance: caches, branch predictors, …. The state of these components is not architectural (i.e., the result of an ordinary application does not depend on it). It is also volatile and cannot be directly monitored by the user. On the other hand, every operating system interrupt modifies thousands of these binary volatile states.In this article, we present and analyze HAVEGE (HArdware Volatile Entropy Gathering and Expansion), a new user-level software heuristic to generate practically strong random numbers on general-purpose computers. The hardware clock cycle counter of the processor can be used to gather part of the entropy/uncertainty introduced by operating system interrupts in the internal states of the processor. Then, we show how this entropy gathering technique can be combined with pseudorandom number generation in HAVEGE. Since the internal state of HAVEGE includes thousands of internal volatile hardware states, it seems impossible even for the user itself to reproduce the generated sequences.

We present a random bit generator that uses fingerprint image as the source of random, and the random bit generator is the first generator in the world that uses biometric information for the source of random in the world. The generator produces, on the average, 9,334 bits a fingerprint image in 0.03 second, and the produced bit sequence passes all 16 statistical tests that are recommended by NIST for testing the randomness.

Monte Carlo computations are considered easy to parallelize. However, the results can be adversely affected by defects in the parallel pseudorandom number generator used. A parallel pseudorandom number generator must be tested for two types of correlations--(i) intra-stream correlation, as for any sequential generator, and (ii) inter-stream correlation for correlations between random number streams on different processes. Since bounds on these correlations are difficult to prove mathematically, large and thorough empirical tests are necessary. Many of the popular pseudorandom number generators in use today were tested when computational power was much lower, and hence they were evaluated with much smaller test sizes.This paper describes several tests of pseudorandom number generators, both statistical and application-based. We show defects in several popular generators. We describe the implementation of these tests in the SPRING [ACM Trans. Math. Software 26 (2000) 436; SPRNG--scalable parallel random number generators. SPRNG 1.0--http://www.ncsa.uiuc.edu/ Apps/SPRNG; SPRNG 2.0--http://sprng.cs.fsu.edu] test suite and also present results for the tests conducted on the SPRNG generators. These generators have passed some of the largest empirical random number tests.

In our day-today life, we performs a lot of tasks that involves direct or indirect use of random numbers e.g. Games, lotteries, simulations and most important cryptography and data communication security. Although, the field of pseudo random number generation is important, it is very difficult to find a good generator for several of applications. In present manuscript, we explore the possibility of a new Pseudorandom Random Number Generator and gives its testing results on NIST test battery.

Random unique number generator can be used for generating a series of unpredictable and unrepeatable numbers within limited ranges of data and numbers. These numbers are usually distributed equally, random, independent, unpredictable and unrepeatable. A good random number generator has to be effective for a long period and has good statistical distribution and efficient generating performance. This study proposes a computational methodology to generate pseudo-random numbers based on random base polynomial, which uses less memory but generates a great deal of unrepeated pseudo-random numbers. Then this method adopts the multi-thread parallelization to effectively get the benefits of multi-core processors to accelerate the generation of a huge amount of pseudo-random numbers.

Random number sequences and RNGs play an important role in trusted computing environments and cryptographic applications. For example, we use random numbers in the generation of keys in TPM. In some web protocols, random numbers are applied to resist replay attacks. It is necessary to guarantee the quality of RNGs and their random sequences because deterministic factors are likely to be involved in the generation process. If a random number generator is not designed carefully, then the output number sequences may become predictable and bring high security risks. Thus, the design of random number generators that produce high-quality random number sequences has been a hot research topic in these decades. Recently, with the development of resource constrained environments, the demand of lightweight random number generators dramatically increases. People prefer to use the random number generators with extreme high efficiency in the on-the-fly applications. This will affect the security performance of the generators. In this paper, we design a random number generator which meets the current lightweight requirements in the resource-limited environments. Our design is originally based on a lightweight block cipher, and applies the property of random looking output of block cipher to the random number generators. We combine a traditional encryption mode with a novel structure for the random number generator, so that the trade-off between security and efficiency can be made perfectly. We also take a comprehensive security evaluation for our random number generator.

Random number is widely used in cryptographic applications, which is mainly used as key. Because the security of key totally depends on the amount and randomness of itself, it's very important to produce random numbers for cryptographic applications. This paper presents a method to generate random numbers for cryptographic applications. NIST Statistical Test Suite which provides 15 statistical methods is used to test the randomness of the random number generated by this method. Because the tests focus on a variety of different types of non-randomness, not all tests are needed. The chosen statistical tests are Frequency (Monobit) Test, Frequency Test within a Block, The Cumulative Sums (Cusums) Test, The Runs Test, Test for the Longest Run of Ones in a Block, Discrete Fourier Transform (Specral) Test, Approximate Entropy Test and Serial Test. The result of tests shows that the random number generated by the random number generator is random. Therefore the conclusion is the random number generated is random enough for cryptographic applications.

The design and implementation of FPGA-and-USB-based control board for quantum experiments are discussed. The usage of quantum true random number generator, control- logic in FPGA and communication with computer through USB protocol are proposed in this paper. Programmable controlled signal input and output ports are implemented. The error-detections of data frame header and frame length are designed. This board has been used in our decoy-state based quantum key distribution (QKD) system successfully.

Pseudorandom number generators are used for session key generation, simulation and games, and other applications that need long bit sequences which posses a lot of qualities like large cycle length and uniform distribution over the range of number domain. There are many state of art algorithms present. This paper is the result of an effort to develop a new Pseudorandom Number Generator that can be used for non-cryptographic applications. The paper includes concept of the new random number generator, its statistical testing result and related proofs.

In this paper, we propose a new lightweight L'Ecuyer-based pseudo random number generator (PRNG). We show that our scheme, despite the very simple functions on which it relies on, is strongly secure in the sense that our number sequences pass the state-of-the-art randomness tests and, importantly, an accurate and deep security analysis shows that it is resistant to a number of attacks.

A pseudorandom number generator that was proposed by Luscher and coded in Fortran by James has been converted to Intel assembly language for PCs, with an increase in speed.

This paper describes hardware implementation of random number generator for session key generation in multiuser high capacity encryption systems. The generator is implemented by non-uniform quantization of avalanche diode noise samples which are modulo 2 added with pseudo random sequence. Physical process statistics stability is achieved by wide dynamic range automatic gain control circuitry of noise amplification stage. Statistic tests of measured results prove the correct operation of implemented device up to 320Mbit/s.

Random Number Generator (RNG) plays an essential role in many sensor network systems and applications, such as security and robust communication. We have developed the first digital hardware random number generator (DHRNG). DHRNG has a small footprint and requires ultra-low energy. It uses a new recursive structure that directly targets efficient FPGA implementation. The core idea is to place or extract random values in FPGA configuration bits and randomly connect the building blocks. We present our architecture, introduce accompanying protocols for secure public key communication, and adopt the NIST randomness test on the DHRNG's output stream.

Linear Feedback Shift Register (LFSR) is mostly used in the implementation of Pseudo Random Number Generator (PRNG). LFSR based on PRNG techniques are used for many applications such as generating data, encryption keys and generating padding bits. Although a majority of random number generators have been implemented in software level, hardware implementation is becoming more and more popular due to the advent of faster and high density Field Programmable Gate Arrays (FPGA). In this paper, we propose implementations of FPGA Shrinking Generator (SG) and Alternating Step Generator (ASG) using LFSR based random number generation. Both systems have been implemented on Altera Cyclone IV board, and random number has been generated in the real time. Generated numbers have been tested according to National Institute of Standards and Technology (NIST) statistical test. According to the results, both SG and ASG have been shown to able to use in the cryptographic systems.

Cryptography techniques rely on chains of random numbers used to generate safe encryption keys. Since random number generator algorithms are in fact pseudo-random their behavior can be predicted if the generation method is known and as such they cannot be used for perfectly safe communications. In this article, we present a perfectly random generator based on quantum measurement processes. The main advantage of such a generator is that using quantum mechanics, its behavior cannot be predicted in any way. We verify the randomness of our generator and compare it to commonly used pseudo-random generators.

Random numbers are very useful in simulation, chaos theory, game theory, information theory, pattern recognition, probability theory, quantum mechanics, statistics, and statistical mechanics. The random numbers are especially helpful in cryptography. In this work, the proposed random number generators come from white noise of audio and video (A/V) sources which are extracted from high-resolution IPCAM, WEBCAM, and MPEG-1 video files. The proposed generator applied on video sources from IPCAM and WEBCAM with microphone would be the true random number generator and the pseudorandom number generator when applied on video sources from MPEG-1 video file. In addition, when applying NIST SP 800-22 Rev.1a 15 statistics tests on the random numbers generated from the proposed generator, around 98% random numbers can pass 15 statistical tests. Furthermore, the audio and video sources can be found easily; hence, the proposed generator is a qualified, convenient, and efficient random number generator.

This paper proposes a well-performing hybrid-type truly quantum random number generator based on the time interval between two independent single-photon detection signals, which is practical and intuitive, and generates the initial random number sources from a combination of multiple existing random number sources. A time-to-amplitude converter and multichannel analyzer are used for qualitative analysis to demonstrate that each and every step is random. Furthermore, a carefully designed data acquisition system is used to obtain a high-quality random sequence. Our scheme is simple and proves that the random number bit rate can be dramatically increased to satisfy practical requirements.

We present a new version of TRQS package for Mathematica computing system. The package allows harnessing quantum random number generators (QRNG) for investigating the statistical properties of quantum states. It implements a number of functions for generating random states. The new version of the package adds the ability to use the on-line quantum random number generator service and implements new functions for retrieving lists of random numbers. Thanks to the introduced improvements, the new version provides faster access to high-quality sources of random numbers and can be used in simulations requiring large amount of random data., Comment: New version of the package described in arXiv:1102.4598. Software available at http://www.iitis.pl/~miszczak/trqs

A high-speed continuous-variable quantum random bit generator with an expected effective bit generation rate of up to 10GBit/s is presented. The obtained bit sequences are truly random and unique, i.e. they cannot be known by an adversary. (c) 2011 Optical Society of America

An important building block for many cryptographic systems is a random number generator. Random numbers are required in these systems, because they are unpredictable for potential attackers. These random numbers can either be generated by a truly random physical source (that is non-deterministic) or using a deterministic algorithm. In practical applications where relatively large amounts of random bits are needed, it is also possible to combine both of these generator types. A non-deterministic random number generator is used to provide a truly random seed, which is used as input for a deterministic algorithm that generates a larger amount of (pseudo-)random bits. In cryptographic systems where Physical Unclonable Functions (PUFs) are used for authentication or secure key storage, an interesting source of randomness is readily available. Therefore, we propose the construction of a FIPS 140-3 compliant random bit generator based on an SRAM PUF in this paper. These PUFs are a source of instant randomness, which is available when powering an IC. Based on large sets of measurements, we derive the min-entropy of noise on the start-up patterns of SRAM memories. The min-entropy determines the compression factor of a conditioning algorithm, which is used to extract a truly random (256 bits) seed from the memory. Using several randomness tests we prove that the conditioned seed has all the properties of a truly random string with full entropy. This truly random seed can be derived in a low cost and area efficient manner from the standard IC component SRAM. Furthermore, an efficient implementation of a deterministic algorithm for generating (pseudo-)random output bits will be proposed. Combining these two functions leads to an ideal way to generate large amounts of random data based on non-deterministic randomness.