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GM/T 0103-2021 English PDF (GMT0103-2021)

GM/T 0103-2021 English PDF (GMT0103-2021)

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GM/T 0103-2021: General framework of random number generator

This document is an overall upper standard for random number generator design; it specifies the overall framework for random number generator design. This document is applicable to the research, development, and testing of random number generators. It can also promote the formulation of relevant standards for random number generators.
GM/T 0103-2021
GM
CRYPTOGRAPHY INDUSTRY STANDARD
OF THE PEOPLE REPUBLIC OF CHINA
ICS 35.030
CCS L 80
General framework of random number generator
ISSUED ON: OCTOBER 18, 2021
IMPLEMENTED ON: MAY 01, 2022
Issued by: National Cryptography Administration
Table of Contents
Foreword ... 3
1 Scope ... 4
2 Normative references ... 4
3 Terms and definitions ... 4
4 Overall framework of random number generator design ... 6
4.1 Overview ... 6
4.2 Entropy... 7
4.3 Entropy evaluation ... 8
4.4 Post-processing ... 8
4.5 Testing ... 9
Appendix A (Informative) Standard system framework of random number generator10 References ... 12
General framework of random number generator
1 Scope
This document is an overall upper standard for random number generator design; it specifies the overall framework for random number generator design.
This document is applicable to the research, development, and testing of random number generators. It can also promote the formulation of relevant standards for random number generators.
2 Normative references
The contents of the following documents constitute the essential provisions of this document through normative references in the text. Among them, for dated references, only the version corresponding to the date is applicable to this document; for undated references, the latest version (including all amendments) is applicable to this document. GB/T 25069 Information security technology - Glossary
GB/T 32915 Information security technology - Binary sequence randomness detection method
GM/T 0062 Random number test requirements for cryptographic modules
GM/T 0078-2020 The design guidelines for cryptographic random number
generation module
GM/T 0105 Design guide for software-based random number generators
GM/Z 4001 Cryptographic terminology
3 Terms and definitions
The terms and definitions as defined in GB/T 25069, GB/T 32915, GM/T 0062, GM/T 0078, GM/T 0105, GM/Z 4001, as well as the following terms and definitions, apply to this document.
3.1
Entropy source
A component, device, or event that produces an output. When this output is captured and processed in some way, a bitstring containing an entropy is produced. [Source: GB/T 25069-2010, 2.1.31]
3.2
Thermal noise
Typically unwanted, but inherently generated spurious electrical signals (also known as "white noise") in components (such as operational amplifiers, reverse-biased diodes, or resistors).
Note: Usually every effort is made to minimize this phenomenon. However, the unpredictability of this phenomenon can be exploited as a source of entropy, in random bitstream generation.
[Source: GB/T 25069-2010, 2.2.4.8]
3.3
Chaotic oscillation
The complex and disordered oscillation state of a nonlinear system.
Note: Rooted in the local instability of the system, it manifests as initial value sensitivity and inherent randomness.
3.4
Phase jitter
Rapid, short-term, random fluctuations in wave phase, which is caused by temporal instabilities.
3.5
Quantum random process
A random phenomenon/process, which has intrinsic quantum randomness.
Note: Its random nature is explained and guaranteed by the principle of quantum mechanics. The quantum stochastic process, which is used to generate random numbers, generally includes single-photon path selection, the number of photons contained in an optical pulse, the time interval between adjacent photons, vacuum fluctuations, laser phase noise, amplified spontaneous emission noise.
3.6
Random number generator
A device or program for generating random binary sequences.
[Source: GB/T 32915-2016, 2.2]
3.7
Software-based RNG
The random number generator component in the software cryptographic module (or the software component of the hybrid cryptographic module), which can be used either as the software cryptographic module alone, or as a part of the software cryptographic module (or the software part of the hybrid cryptographic module). [Source: GM/T 0105-2021, 3.13]
3.8
Raw random number sequence
A sequence of discrete random values obtained through digitizing the outputs of entropy source.
3.9
Random number sequence
A sequence of numbers, in which each term cannot be inferred, given the knowledge of the other terms.
[Source: GB/T 25069-2010, 2.2.2.184]
4 Overall framework of random number generator design
4.1 Overview
The random number generator's design framework is as shown in Figure 1. The random number generator usually includes entropy source, post-processing, testing. In the design stage, entropy evaluation is performed on the entropy source or random source sequence, whilst in the product testing and use phase, the validity test or randomness test is performed on the random source sequence or random number sequence. characteristics of uncertain events in the real world, such as measuring thermal noise level values, etc. The theoretical stochastic model of physical entropy is clear and reasonable; the rationality of the claimed stochastic model can be verified, through the collected sample data. The entropy of the physical entropy source output shall be theoretically estimated, meanwhile the estimated value must be greater than a certain threshold, to ensure that the output has enough entropy.
b) Non-physical entropy sources refer to non-deterministic entropy sources, that do not belong to physical entropy sources, such as collecting mouse or keyboard actions, etc. The non-physical entropy source is provided by the operating environment, where the random number generator is located (such as the
operating system, external devices), so certain precautions shall be taken, to reduce the possibility of the adversary cracking the non-physical entropy source (such as the predicted output). The sufficiency and stability of the entropy output by the non-physical entropy source can be demonstrated, by modeling or
experiments.
The entropy source is the source of the random number, which is generated by the random number generator. When the entropy source fails, it needs to be quickly detected by the random number generator, meanwhile corresponding processing shall be done according to the testing output, such as generating an alarm signal. 4.3 Entropy evaluation
The entropy evaluation predicts and evaluates the random source sequence, through theoretical modeling analysis, statistical testing and other methods, to obtain the entropy estimate.
According to the different design principles of entropy sources, select the applicable entropy evaluation method. The entropy evaluation method shall be reasonable and effective; the estimated value shall be greater than a certain threshold, such as 0.997. Entropy evaluation may not be implemented, inside the random number generator. 4.4 Post-processing
The post-processing module processes the random source sequence; generates a random number sequence, that meets the statistical testing, through a post-processing algorithm. The post-processing module is optional; in practice, it shall be decided to select it or not, according to the statistical characteristics of the random source sequence. There are many post-processing algorithms, such as cryptographic function post- processing method based on block ciphers, hash functions, m-sequences, etc., as well as the light post-processing method such as Von Neumann corrector, XOR chain, parity grouping, m-LSB, etc. The design can be carried out, according to the characteristics of

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