EFFICIENT QUANTUM RANDOM NUMBER GENERATION USING QUANTUM INDISTINGUISHABILITY

2013 ◽  
Vol 12 (04) ◽  
pp. 1350020 ◽  
Author(s):  
H. AKSHATA SHENOY ◽  
R. SRIKANTH ◽  
T. SRINIVAS
Keyword(s):  
Efficient Method ◽  
Random Number ◽  
Random Number Generation ◽  
Random Numbers ◽  
Quantum Method ◽  
Number Generation ◽  
Quantum Indistinguishability ◽  
Path Dependent

In this paper, we propose a quantum method for generation of random numbers based on bosonic stimulation. Randomness arises through the path-dependent indeterministic amplification of two competing bosonic modes. We show that the process provides an efficient method for macroscopic extraction of microscopic randomness.

scholarly journals True Random Number Generation from Bioelectrical and Physical Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Seda Arslan Tuncer ◽  
Turgay Kaya
Keyword(s):  
Blood Volume ◽  
Random Number ◽  
Logistic Map ◽  
Random Number Generation ◽  
Statistical Properties ◽  
Random Numbers ◽  
Number Generation ◽  
Skin Response ◽  
Volume Pulse ◽  
Blood Volume Pulse

It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. 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. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. 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). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. 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. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.

A Low-Cost Highly Reliable Spintronic True Random Number Generator Circuit for Secure Cryptography

SPIN ◽  
2019 ◽  
Vol 10 (01) ◽  
pp. 2050003 ◽  
Author(s):  
Iman Alibeigi ◽  
Abdolah Amirany ◽  
Ramin Rajaei ◽  
Mahmoud Tabandeh ◽  
Saeed Bagheri Shouraki
Keyword(s):  
High Speed ◽  
High Performance ◽  
Random Number ◽  
Low Cost ◽  
Random Number Generator ◽  
Magnetic Tunnel Junction ◽  
Random Number Generation ◽  
Cmos Technology ◽  
Random Numbers ◽  
Number Generation

Generation of random numbers is one of the most important steps in cryptographic algorithms. High endurance, high performance and low energy consumption are the attractive features offered by the Magnetic Tunnel Junction (MTJ) devices. Therefore, they have been considered as one of the promising candidates for next-generation digital integrated circuits. In this paper, a new circuit design for true random number generation using MTJs is proposed. Our proposed circuit offers a high speed, low power and a truly random number generation. In our design, we employed two MTJs that are configured in special states. Generated random bit at the output of the proposed circuit is returned to the write circuit to be written in the relevant cell for the next random generation. In a random bitstream, all bits must have the same chance of being “0”or “1”. We have proposed a new XOR-based method in this paper to resolve this issue in multiple random generators that produce truly random numbers with a different number of ones and zeros in the output stream. The simulation results using a 45[Formula: see text]nm CMOS technology with a special model of MTJ validated the advantages offered by the proposed circuit.

scholarly journals Twister Generator of Stochastic Planes

2019 ◽  
pp. 27-45
Author(s):  
A.F. Deon ◽  
V.A. Onuchin ◽  
Yu.A. Menyaev
Keyword(s):  
Random Number ◽  
Random Access ◽  
Random Number Generation ◽  
Random Numbers ◽  
Access Memory ◽  
Number Generation ◽  
Novel Approach ◽  
Pseudorandom Number Generation ◽  
Sequence Parameters ◽  
Uniform Random Numbers

Various pseudorandom number generation algorithms may be used to create a discrete stochastic plane. If a Cartesian completeness property is required of the plane, it must be uniform. The point is, employing the concept of uncontrolled random number generation may yield low-quality results, since original sequences may omit random numbers or not be sufficiently uniform. We present a novel approach for generating stochastic Cartesian planes according to the model of complete twister sequences featuring uniform random numbers without omissions or repetitions. Simulation results confirm that the random planes obtained are indeed perfectly uniform. Moreover, recombining the original complete uniform sequence parameters allows the number of planes created to be significantly increased without using any extra random access memory.

scholarly journals The influence of random number generation in dissipative particle dynamics simulations using a cryptographic hash function

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250593
Author(s):  
Kiyoshiro Okada ◽  
Paul E. Brumby ◽  
Kenji Yasuoka
Keyword(s):  
Hash Function ◽  
Random Number ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Random Number Generation ◽  
Memory Systems ◽  
Particle Dynamics ◽  
Random Numbers ◽  
Number Generation

The tiny encryption algorithm (TEA) is widely used when performing dissipative particle dynamics (DPD) calculations in parallel, usually on distributed memory systems. In this research, we reduced the computational cost of the TEA hash function and investigated the influence of the quality of the random numbers generated on the results of DPD calculations. It has already been established that the randomness, or quality, of the random numbers depend on the number of processes from internal functions such as SHIFT, XOR and ADD, which are commonly referred to as “rounds”. Surprisingly, if we choose seed numbers from high entropy sources, with a minimum number of rounds, the quality of the random numbers generated is sufficient to successfully perform accurate DPD simulations. Although it is well known that using a minimal number of rounds is insufficient for generating high-quality random numbers, the combination of selecting good seed numbers and the robustness of DPD simulations means that we can reduce the random number generation cost without reducing the accuracy of the simulation results.

Uniform Random Number Generation With Jumping Facilities

2018 ◽  
pp. 1297-1306
Author(s):  
E. Jack Chen
Keyword(s):  
Variance Reduction ◽  
Random Number ◽  
Simple Procedure ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Number Generation ◽  
Multiple Recursive Generator ◽  
Generating Sequences

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined Multiple Recursive Generator) combines two multiple recursive random number generators with a period length of approximately 2191 (˜ 3.1× 1057), good speed, and excellent theoretical properties.

UNBIASED QUANTUM RANDOM NUMBER GENERATION BASED ON SQUEEZED VACUUM STATE

2012 ◽  
Vol 10 (01) ◽  
pp. 1250012 ◽  
Author(s):  
YANYANG ZHU ◽  
GUANGQIANG HE ◽  
GUIHUA ZENG
Keyword(s):  
Random Number ◽  
Quantum Noise ◽  
Random Number Generation ◽  
Vacuum State ◽  
Random Numbers ◽  
Squeezed Vacuum State ◽  
Quantum Randomness ◽  
Number Generation ◽  
Squeezed Vacuum ◽  
The One

In this paper, a new quantum random number generation scheme which is implemented by measuring quantum noise of the squeezed vacuum state is proposed. In the proposed scheme, the Shannon entropy is employed to measure randomness of the generated random numbers. In addition, some characteristics of the generated random numbers are investigated. To reach the pure quantum randomness, an extraction approach based on universal hash functions for the generated quantum random numbers is presented. Results show that the proposed scheme based on squeezed vacuum state has remarkable advantages over the one associated with the vacuum state.

On the Generation of Random Numbers for Symmetric Cryptography Utilizing Astronomical Data

2011 ◽  
Vol 367 ◽  
pp. 185-190
Author(s):  
P.M. Rubesh Anand ◽  
Vidhyacharan Bhaskar ◽  
Gaurav Bajpai ◽  
Godwin Norense Osarumwense Asemota
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
The Other ◽  
Rank Test ◽  
Random Numbers ◽  
Symmetric Cryptography ◽  
Astronomical Data ◽  
Number Generation ◽  
Astronomical Object

In this paper, a novel method for obtaining the random numbers utilizing astronomical data is proposed. The method uses two different algorithms for generation of random numbers sequence. Astronomical data collected from the scientific study of the universe, especially of the relative motions, relative positions of astronomical objects are utilized in our algorithms. The first algorithm uses a particular astronomical object in a fixed position for the random number generation. The random sequence is obtained from the relative positions of other astronomical objects with reference to the selected object. The second algorithm selects any diverse astronomical object as a reference in a varying mode for computation of the relative positions of different objects with that reference to generate the random number stream. Both algorithms use mathematical equations for computing the next jump or hop to the other astronomical object. The generated random numbers obtained from the two algorithms are tested with a standard statistical test suite including, frequency test, run test, random binary matrix rank test, complexity test, universal test and entropy test. The results obtained from the statistical tests of the two algorithms are compared with the other publicly available random number generation techniques, like, linear congruential and modular exponentiation. The preliminary results show that the algorithms perform well. The random numbers generated by our method has sufficient period and unpredictability that makes them suitable for consideration as encryption keys in symmetric cryptography.

Uniform Random Number Generation With Jumping Facilities

2019 ◽  
pp. 337-349
Author(s):  
E. Jack Chen
Keyword(s):  
Variance Reduction ◽  
Random Number ◽  
Simple Procedure ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Number Generation ◽  
Multiple Recursive Generator ◽  
Generating Sequences

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined multiple recursive generator) combines two multiple recursive random number generators with a period length of approximately 2191 (≈ 3.1× 1057), good speed, and excellent theoretical properties.

scholarly journals Memristor based ring oscillators true random number generator with different window functions for applications in cryptography

2019 ◽  
Vol 14 (1) ◽  
pp. 201 ◽  
Author(s):  
Noor Alia Nor Hashim ◽  
Julius Teo Han Loong ◽  
Azrul Ghazali ◽  
Fazrena Azlee Hamid
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Ring Oscillators ◽  
Number Generator ◽  
True Random Number Generator ◽  
Number Generation ◽  
Window Functions

<span>Cryptographic applications require numbers that are random and pseudorandom. Keys must be produced in a random manner in order to be used in common cryptosystems. Random or pseudorandom inputs at different terminals are also required in a lot of cryptographic protocols. For example, producing digital signatures using supporting quantities or in verification procedures that requires generating challenges. Random number generation is an important part of cryptography because there are flaws in random number generation that can be taken advantage by attackers that compromised encryption systems that are algorithmically secure. True random number generators (TRNGs) are the best in producing random numbers. This paper presents a True Random Number Generator that uses memristor based ring oscillators in the design. The designs are implemented in 0.18 µm complementary metal oxide semiconductor (CMOS) technology using LT SPICE IV. Different window functions for the memristor model was applied to the TRNG and compared. Statistical tests results of the output random numbers produced showed that the proposed TRNG design can produce random output regardless of the window function.</span>

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