Use Of Pseudo-random Number Generators Research Articles

Experimental and numerical investigation of strain inhomogeneity in zirconia during a Brazilian test

Zirconia-based ceramics is a material with a wide variety of applications. Problems of strength of such ceramics, being prepared by different methods, under different loading conditions, and with different porosity, are of practical importance. Inhomogeneous strain distributions in different-porosity zirconia ceramics during a Brazilian test are studied using the digital image correlation and numerical simulation techniques. Spatiotemporal patterns of strain localization across the compression axis are obtained. It is found that strain components are nonuniformly distributed over the specimen surface, and zirconia experiences macroscopically localized deformation. The fracture patterns reveal the triple-cleft mode. Numerical simulations performed both for a homogeneous material and with consideration for the material inhomogeneity show that different models yield different patterns of inhomogeneous strain distribution under diametral compression. Account for internal friction and dilatancy provides eight-shaped cracks. Random distributions of strength and elastic properties over the computational domain built with the use of pseudo-random number generators and/or Gaussian random fields allow one to control the size of strain inhomogeneities.

APPLICATION OF A PSEUDO-RANDOM NUMBER GENERATOR TO INCREASE THE EFFICIENCY OF SMART DUST TECHNOLOGY IN SMART HOME CONTROL

The article is devoted to the use of pseudo-random number generators to improve the efficiency of Smart Dust technology in the context of smart home management. The article examines the problem of ensuring reliable and safe interaction between Smart Dust technology and the Internet of Things (IoT) in the context of smart home management. To solve this problem, it is proposed to use generators of pseudo-random numbers. The article provides a detailed overview of Smart Dust and IoT technology, including their application in smart home management. The advantages and disadvantages of Smart Dust and IoT technology are analyzed, their possible problems with security and reliability of interaction are indicated. Next, the article describes in detail the principles of pseudorandom number generators and their use in Smart Dust technology. In particular, research is presented on how the use of pseudo-random numbers can help improve the security and reliability of the interaction between Smart Dust technology and IoT. The article also presents a mathematical model of using Smart Dust technology using pseudo-random number generators in smart home management. The algorithm of operation of the Smart Dust technology using a generator of pseudo-random numbers in a smart house has been built. The article provides an analysis of the effectiveness of using pseudorandom number generators for Smart Dust technology in smart home management. For this, experiments were conducted that showed that the use of pseudorandom number generators increases the efficiency of Smart Dust technology and reduces the risk of errors and incorrect system operation. The prospects of using a sequence of pseudo-random numbers in Smart Dust technology are characterized.

Higher-Order Lookup Table Masking in Essentially Constant Memory

Masking using randomised lookup tables is a popular countermeasure for side-channel attacks, particularly at small masking orders. An advantage of this class of countermeasures for masking S-boxes compared to ISW-based masking is that it supports pre-processing and thus significantly reducing the amount of computation to be done after the unmasked inputs are available. Indeed, the “online” computation can be as fast as just a table lookup. But the size of the randomised lookup table increases linearly with the masking order, and hence the RAM memory required to store pre-processed tables becomes infeasible for higher masking orders. Hence demonstrating the feasibility of full pre-processing of higher-order lookup table-based masking schemes on resource-constrained devices has remained an open problem.
 In this work, we solve the above problem by implementing a higher-order lookup table-based scheme using an amount of RAM memory that is essentially independent of the masking order. More concretely, we reduce the amount of RAM memory needed for the table-based scheme of Coron et al. (TCHES 2018) approximately by a factor equal to the number of shares. Our technique is based upon the use of pseudorandom number generator (PRG) to minimise the randomness complexity of ISW-based masking schemes proposed by Ishai et al. (ICALP 2013) and Coron et al. (Eurocrypt 2020). Hence we show that for lookup table-based masking schemes, the use of a PRG not only reduces the randomness complexity (now logarithmic in the size of the S-box) but also the memory complexity, and without any significant increase in the overall running time. We have implemented in software the higher-order table-based masking scheme of Coron et al. (TCHES 2018) at tenth order with full pre-processing of a single execution of all the AES S-boxes on a ARM Cortex-M4 device that has 256 KB RAM memory. Our technique requires only 41.2 KB of RAM memory, whereas the original scheme would have needed 440 KB. Moreover, our 8-bit implementation results demonstrate that the online execution time of our variant is about 1.5 times faster compared to the 8-bit bitsliced masked implementation of AES-128.

Strengthening security of Mobile, IOT and Edge applications in Post-Quantum era using Quantum Random Number Generators

Mobile devices have become a gold mine for attackers because of the increased and rather unplanned boom in digital activity. Recently there is more demand for interconnected IOT and edge computing services as well. All this increased the value and amount of data in these devices. With the increase in sensitivity of data available in these devices, we see more and more powerful attacks every day for breaking the existing cryptographic systems. Advancements in Quantum Technology is another potential threat for existing cryptographic standards. Random Number Generators (RNG) are a primitive tool required for securing these devices and computations, starting from their role in generating good and unpredictable encryption keys. Today, the vast majority of Cryptographic implementations make use of Pseudo-Random Number Generators which are prone to existing classical and Quantum attacks. Careful choice of True Random Number Generators is the best way to go. In this paper, we did an in-depth investigation on the different key generation techniques used in existing cryptographic schemes and the impact of quantum computers on their security. Then, we give an overview on the Random Numbers and different types of Random Number Generators. Next, we justify that the Quantum Random Number Generators based on Quantum Mechanics possess the desirable True Randomness and why we need quantum-safe key generation that can be an immediate step in strengthening security against both classical and quantum attacks. Finally, we introduce different types of QRNG implementations along with their challenges, drawbacks and suitability for Mobile, IOT and Edge applications.

GALOIS GENERALIZED MATRICES IN STREAM CIPHERS

The matrix terms Galois and Fibonacci borrowed from the theory of cryptography. These matrices connected by so-called right-hand transposition (a transposing of the relative to the auxiliary diagonal). In cryptography makes extensive use of pseudorandom number generators in Galois and Fibonacci schemes. With the help of these matrices, the same binary sequence can form as the LFSRs generated. In addition to the matrices named in work, other matrices have introduced. These include conjugate matrices Galois and Fibonacci, those created by classical (the left-hand) transpose, inverse to the basis matrices, and those inverse to the conjugate matrices. Traditional pseudorandom number generators have a significant disadvantage, which is that they are subject to the Berlekemp-Messi attack. Two main approaches proposed to prevent such attacks. The first of them assume the change from classical generators to generalized pseudorandom number generators. The second constructive way of protection against the Berlekemp-Messi attack is the construction of generators pseudorandom number based on transformations of similarity of traditional or generalized generators. This study aims to develop algorithms for the synthesis of generalized Galois of the maximum period and to establish interrelationships of Galois matrices.

Comparison of the order picking processes duration based on data obtained from the use of pseudorandom number generator

Abstract In this paper a simulation model of a part of logistics facility, i.e. an order picking area in a high-bay warehouse, is considered. The construction of this model was preceded by the development of a conceptual model as a result of the study for selected warehouses of this type. The researched simulation model is not a reflection of a logistics facility functioning in reality. This facility is a hypothetical warehouse. The simulation model includes generators of pseudorandom numbers, which, among other things, serve to obtain and introduce randomly generated picking orders (picking lists) into the model. And at the same time these generators reflect and provide the real, often unforeseen, size of orders. This allows, among other things, to analyze the capacity of the considered facility. The paper presents a comparison of results obtained with use of simulation model for pseudorandom number generator with different data at the input to these model.

Myriad

The current research focus on Big Data systems calls for a rethinking of data generation methods. The traditional sequential data generation approach is not well suited to large-scale systems as generating a terabyte of data may require days or even weeks depending on the number of constraints imposed on the generated model. We demonstrate Myriad , a new data generation toolkit that enables the specification of semantically rich data generator programs that can scale out linearly in a shared-nothing environment. Data generation programs built on top of Myriad implement an efficient parallel execution strategy leveraged by the extensive use of pseudo-random number generators with random access support.

How much impact does the choice of a random number generator really have?

This article considers the results of Van Niel, K. and Laffan, S. W. 2003. Gambling with randomness; the use of pseudo-random number generators in GIS. International Journal of Geographical Information Science, 17, 49–68 and demonstrates flaws in their analysis and discusses the implications of these. In conclusion, it argues that difficulties with random number generators may have been overemphasised in Van Niel, K. and Laffan, S. W. 2003. Gambling with randomness; the use of pseudo-random number generators in GIS. International Journal of Geographical Information Science, 17, 49–68 and that it may make more practical sense to focus on the model formulation and analysis rather than the technical choice of one random number generator over another. In particular, it argues that the choice of random number generator should occur at the analysis stage, rather than relying on empirical verification.

Ant System Implementation using Microblaze: Some Preliminary Results on Efficiency Study

The paper presents some preliminary results on efficiency study of Ant System implementation using software processor Microblaze. By the use of Monte-Carlo tests of number π calculation the best use of Pseudo-Random Number Generator – implementation of Multiply-With-Carry algorithm in a single precision floating point numbers – is grounded. By experimentation the usefulness of eight supplemental Microblaze core units is assessed and the advantage of the use of Basic Floating Point Unit together with 32 bits Integer Multiplier is proven. Experimental investigation of Traveling Salesman Problem solution by implemented Ant System is presented and confirms that rapid growth of standard deviation may be used as an indicator that system should be adjusted for current complexity of the problem. Ill. 2, bibl. 13. (in English; summaries in English, Russian and Lithuanian).

Gambling with randomness: the use of pseudo-random number generators in GIS

Analyses within the field of GIS are increasingly applying stochastic methods and systems that make use of pseudo-random number generators (PRNGs). Examples include Monte Carlo techniques, dynamic modelling, stochastic simulation, artificial life and simulated data development. PRNGs have inherent biases, and this will in turn bias any analyses using them. Therefore, the validity of stochastic analyses is reliant on the PRNG employed. Despite this, the effect of PRNGs in spatial analyses has never been completely explored, particularly a comparison of different PRNGs. Exacerbating the problem is that GIS articles applying Monte Carlo or other stochastic methods rarely report which PRNG is employed. It thus appears likely that GIS researchers rarely, if ever, check the suitability of the PRNG employed for their analyses or simulations. This paper presents a discussion of some of the characteristics of PRNGs and specific issues from a geospatial standpoint, including a demonstration of the differences in the results of a Monte Carlo analysis obtained using two different PRNGs. It then makes recommendations for the application of PRNGs in spatial analyses, including recommending specific PRNGs that have attributes appropriate for geospatial analysis. The paper concludes with a call for more research into the application of PRNGs to spatial analyses to fully understand the impact of biases, especially before they are routinely used in the wider spatial analysis community.

081050 (E10) Recommendations on the testing and use of pseudo-random number generators used in Monte Carlo analysis for risk assessment: Barry T.M., United States Environmental Protection Agency, Washington, Risk Analysis, Vol. 16, Number 1, 1996, pp. 93–105

081050 (E10) Recommendations on the testing and use of pseudo-random number generators used in Monte Carlo analysis for risk assessment: Barry T.M., United States Environmental Protection Agency, Washington, Risk Analysis, Vol. 16, Number 1, 1996, pp. 93–105

Recommendations on the Testing and Use of Pseudo-Random Number Generators Used in Monte Carlo Analysis for Risk Assessment

Recommendations on the Testing and Use of Pseudo-Random Number Generators Used in Monte Carlo Analysis for Risk Assessment