• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.253-257
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• 2012

This paper presents an efficient filtering system for a metastable state-based true random number generator. To output a result with high randomness, we use loop-storage for storing the value of metastability. During the metastable state, the output value is accumulated to the storage. When the non-metastable state arises, the stored metastable value will be used for output instead of the result of the non-metastable state. As a result, we can maintain high entropy together with the original throughput.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.878-881
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• 2009

As for the technology developed for network security, there is little difference of design ability between the domestic and the foreign studies. Although the development of 2048 RSA processor has been undergone, the processing speed does not meet the requirement due to its long width. These days, an RSA processor architecture with higher speed comsuming less resource is necessary. As for the development of RNG (Random Number Generator), the technology trend is moving from PRNG (Pseudo Random Number Generator) to TRNG (True Random Number Generator), also requiring less area and high speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.803-806
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• 2007

Since the different characteristics from the PRNG (Pseudo Random Number Generator) or various deterministic devices such as arithmetic processing units, new concepts and test methods should be suggested in order to test TRNG (Ture Random Number Generator). Deterministic devices can be covered by ATPG (Automatic Test Pattern Generation), which uses patterns generated by cyclic shift registers due to its hardware oriented characteristics, pure random numbers are not possibly tested by automatic test pattern generation due to its analog-oriented characteristics. In this paper, we studied and analyzed a hardware/software combined test method named Diehard test, in which we apply continuous pattern variation to check the statistics. We also point out the considerations when making random number tests.

• ETRI Journal
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• v.42 no.6
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• pp.951-964
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• 2020

This paper presents a lightweight true random number generator (TRNG) using beta radiation that is useful for Internet of Things (IoT) security. In general, a random number generator (RNG) is required for all secure communication devices because random numbers are needed to generate encryption keys. Most RNGs are computer algorithms and use physical noise as their seed. However, it is difficult to obtain physical noise in small IoT devices. Since IoT security functions are required in almost all countries, IoT devices must be equipped with security algorithms that can pass the cryptographic module validation programs of each country. In this regard, it is very cumbersome to embed security algorithms, random number generation algorithms, and even physical noise sources in small IoT devices. Therefore, this paper introduces a lightweight TRNG comprising a thin-film beta-radiation source and integrated circuits (ICs). Although the ICs are currently being designed, the IC design was functionally verified at the board level. Our random numbers are output from a verification board and tested according to National Institute of Standards and Technology standards.

• Journal of IKEEE
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• v.24 no.1
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• pp.52-58
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• 2020

This paper describes a hardware implementation of a true random number generator (TRNG) for information security applications. A new approach for TRNG design was proposed by adopting random transition rules in cellular automata and applying different transition rules at every time step. The TRNG circuit was implemented on Spartan-6 FPGA device, and its hardware operation generating random data with 100 MHz clock frequency was verified. For the random data of 2×10⁷ bits extracted from the TRNG circuit implemented in FPGA device, the randomness characteristics of the generated random data was evaluated by the NIST SP 800-22 test suite, and all of the fifteen test items were found to meet the criteria. The TRNG in this paper was implemented with 139 slices of Spartan-6 FPGA device, and it offers 600 Mbps of the true random number generation with 100 MHz clock frequency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.755-759
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• 2012

In this paper, it is about random number generator, which is based on hardware is utilized in medical science and game area. The Intel presents guideline of security level about hardware based true random number generator. At hardware based random number generator, the various test items, that are included in test suits as NIST statistical test, FIPS140-1, is applied. In this paper, it experiments about degree extent of randomness variation from filter scheme effects, which is applied in output stream of hardware noise source.

• Journal of IKEEE
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• v.24 no.2
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• pp.543-549
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• 2020

As the importance of security increases in various fields, research on a random number generator (RNG) used for generating an encryption key, has been actively conducted. A high-quality RNG is essential to generate a high-performance encryption key, but the initial pseudo-random number generator (PRNG) has the possibility of predicting the encryption key from the outside even though a large amount of hardware resources are required to generate a sufficiently high-performance random number. Therefore, the demand of high-quality true random number generator (TRNG) generating random number through various noises is increasing. This paper examines and compares the representative TRNG methods based on metastable-based and ring-oscillator-based TRNGs. We compare the methods how the random sources are generated in each TRNG and evaluate its performances using NIST SP 800-22 tests.

• ETRI Journal
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• v.32 no.1
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• pp.93-101
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• 2010

A true random number generator (TRNG) is widely used to generate secure random numbers for encryption, digital signatures, authentication, and so on in crypto-systems. Since TRNG is vulnerable to environmental changes, a deterministic function is normally used to reduce bias and improve the statistical properties of the TRNG output. In this paper, we propose a linear corrector for secure TRNG. The performance of a linear corrector is bounded by the minimum distance of the corresponding linear error correcting code. However, we show that it is possible to construct a linear corrector overcoming the minimum distance limitation. The proposed linear corrector shows better performance in terms of removing bias in that it can enlarge the acceptable bias range of the raw TRNG output. Moreover, it is possible to efficiently implement this linear corrector using only XOR gates, which must have a suitable hardware size for embedded security systems.

• ETRI Journal
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• v.43 no.3
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• pp.571-571
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• 2021