• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.759-761
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• 2010

In this paper, we propose a video encryption method using pseudo-random numbers based on MLCA(Maximal length Cellular Automata). Firstly, we generate a basis image which is composed with pseudo-random numbers, using MLCA. Futhermore, The original video is encrypted by computing XOR operation between the basis image and each frame of original video. The video encryption is conducted in accordance with one or two rules, and is evaluated.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.992-993
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• 2014

임의 숫자는 여러 분야에서 다양하게 사용되고 있으며, 크게 True Random Number와 Pseudo Random Number로 구분지어 지는데, 대부분의 경우 Pseudo Random Number를 사용하고 있다. 이 경우, 동일한 Seed에 대해서는 항상 동일한 값을 반환하기 때문에, 진정한 임의 숫자라고 하기는 어렵다. 본 논문에서는 임의 숫자에 대한 기본 정의와 더불어 정지 영상을 이용하여 임의 숫자를 생성하는 방법에 대해 알아보고, 기존의 Pseudo Random Number와의 차이점을 설명하도록 하겠다.

• Journal of KIISE
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• v.42 no.12
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• pp.1467-1473
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• 2015

In this paper, we implemented a parallel random number generation program on GPU's, which are known for high performance computing, using LCG (Linear Congruential Generator). Random numbers are important in all fields requiring the use of randomness, and LCG is one of the most widely used methods for the generation of pseudo-random numbers. We explained the parallel program using the NVIDIA CUDA model and MPI(Message Passing Interface) and showed uniform distribution and performance results. We also used a Monte Carlo algorithm to calculate pi(${\pi}$) comparing the parallel random number generator with cuRAND, which is a CUDA library function, and showed that our program is much more efficient. Finally we compared performance results using multi-GPU's with those of ideal speedups.

• ETRI Journal
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• v.42 no.4
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• pp.518-526
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• 2020

This paper presents an efficient hardware random-number generator based on a beta source. The proposed generator counts the values of "0" and "1" and provides a method to distinguish between pseudo-random and true random numbers by comparing them using simple cumulative operations. The random-number generator produces labeled data indicating whether the count value is a pseudo- or true random number according to its bit value based on the generated labeling data. The proposed method is verified using a system based on Verilog RTL coding and LabVIEW for hardware implementation. The generated random numbers were tested according to the NIST SP 800-22 and SP 800-90B standards, and they satisfied the test items specified in the standard. Furthermore, the hardware is efficient and can be used for security, artificial intelligence, and Internet of Things applications in real time.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.837-842
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• 2010

In this paper, quantum cryptography systems used in the design process inevitably open bit stream of pseudo-random number that exists multiple open channels between them and the need to share information on the part of the situation exposes a pair of bit stream. In this paper, the base test of pseudo-random number I tested out this process and the merge bit binary column look out for randomness.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.365-378
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• 2024

The operating system (OS) of mobiles or embedded devices is based on the Linux kernel. These OSs request random numbers from the Linux kernel for system operation, such as encryption keys and security features. To provide random numbers reliably, the Linux kernel has a dedicated random number generator (Linux Pseudo Random Number Generator, LPRNG). Recently, LPRNG has undergone a major structural changes. However, despite the major changes, no security analysis has been published on the structure of the new LPRNG. Therefore, we analyze these structural changes as a preliminary study to utilize the security analysis of the new LPRNG. Furthermore, the differences between before and after the changes are divided into cryptographic and performance perspectives to identify elements that require security analysis. This result will help us understand the new LPRNG and serve as a base for security analysis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.51-58
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• 2014

A LFSR is commonly used for various stream cryptography applications to generate random numbers. A Leap-ahead LFSR was presented to generate a multi-bits random number per cycle. It only requires a single LFSR and it has an advantages in hardware complexity. However, it suffers from the significant reduction of maximum period of the generated random numbers. This paper presents the new segmented Leap-ahead LFSR to solve this problem. It consists of two segmented LFSRs. We prove the efficiency of the proposed segmented architecture using the precise mathematical analysis. We also demonstrate the proposed comparison results with other counterparts using Xinilx Vertex5 FPGA. The proposed architecture can increase 2.5 times of the maximum period of generated random numbers compared to the typical Leap-ahead architecture.

• Convergence Security Journal
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• v.12 no.4
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• pp.17-23
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• 2012

Recently, the research for cryptographic algorithm, in particular, a stream cipher has been actively conducted for wireless devices as growing use of wireless devices such as smartphone and tablet. LFSR based random number generator is widely used in stream cipher since it has simple architecture and it operates very fast. However, the conventional multi-LFSR RNG (random number generator) suffers from its hardware complexity as well as very closed correlation between the numbers generated. A leap-ahead LFSR was presented to solve these problems. However, it has another disadvantage that the maximum period of the generated random numbers are significantly decreased according to the relationship between the number of the stages of the LFSR and the number of the output bits of the RNG. This paper presents new leap-ahead LFSR architecture to prevent this decrease in the maximum period by applying segmentation technique to the conventional leap-ahead LFSR. The proposed architecture is implemented using VHDL and it is simulated in FPGA using Xilinx ISE 10.1, with a device Virtex 4, XC4VLX15. From the simulation results, the proposed architecture has only 20% hardware complexity but it can increases the maximum period of the generated random numbers by 40% compared to the conventional Leap-ahead archtecture.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1469-1475
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• 2010

This paper presents a problem of existing encryption methods using pseudo-random numbers based on MLCA or complemented MLCA and proposes a method to resolve this problem. The existing encryption methods have a problem which the edge of original image appear on encrypted image because the image have color similarity of adjacent pixels. In this proposed method, we transform the value and spatial coordinates of all pixels by using pseudo-random numbers based on MLCA. This method can resolve the problem of existing methods and improve the level of encryption by encrypting pixel coordinates and pixel values of original image. The effectiveness of the proposed method is proved by conducting histogram and key space analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6161-6176
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• 2018

Pseudorandom numbers are useful in cryptographic operations for using as nonce, initial vector, secret key, etc. Security of the cryptosystem relies on the secret key parameters, so a good pseudorandom number is needed. In this paper, we have proposed a new approach for generation of pseudorandom number. This method uses the three dimensional combinational puzzle Rubik Cube for generation of random numbers. The number of possible combinations of the cube approximates to 43 quintillion. The large possible combination of the cube increases the complexity of brute force attack on the generator. The generator uses cryptographic hash function. Chaotic map is being employed for increasing random behavior. The pseudorandom sequence generated can be used for cryptographic applications. The generated sequences are tested for randomness using NIST Statistical Test Suite and other testing methods. The result of the tests and analysis proves that the generated sequences are random.