On the Additional Chi-Square Tests for the IID Assumption of NIST SP 800-90B

Kang, Ju-Sung; Park, Hojoong; Yeom, Yongjin

doi:10.1109/pst.2017.00051

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In addition, the systematic mismatch influences the random numbers generated, according to the analytical model. However, an overestimation of the minimum entropy (H min ) is generated for false positives in the IID assumption [38], [39]. In this way, the entropy source is classified with a non-IID assumption for the systematic mismatch in the collapse time.…”

Section: Entropy Testsmentioning

confidence: 99%

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

et al. 2022

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True Random Number Generator (TRNG) is used in many applications, generally for generating random cryptography keys. In this way, the trust of the cryptography system depends on the quality of the random numbers generated. However, the entropy fluctuations produced by external perturbations generate some false positives in the random sequence. These false positives can generate a disastrous scenario, depending on the application. This work presents the results of different tests to demonstrate the robustness and health of the TRNG based on frequency collapse. The TRNG passed all entropy tests provided for NIST SP800-90B and AIS31. The entropy test denotes a 0.9789 minimum entropy normalized and 7.998 Shannon entropy. In addition, the TRNG passes the health tests provided for NIST SP800-90B. The health test shows a number of identical values I v = 0%, I v−1 < 0.004% and a maximum cutoff value M C v = 10 with LM C v = 13 in the repetition count and adaptive proportion tests, respectively. The implementation passed all the statistical tests provided for NIST SP800-22 and AIS20. Besides, the implementation passes the different tests with Process, Voltage, and Temperature (PVT) variations. The TRNG is implemented in a 0.18µm General Purpose (GP) CMOS technology, occupying 25600µm 2 with four entropy sources. Finally, the implementation presents a 7.3 until 9.2-Mb/s of bit rate, 0.56 until 1.88-mW of power consumption, and 77.2 until 204.3-pJ/bit of energy per bit using an entropy source with 16 and 2 delay stages, respectively.

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Section: Entropy Testsmentioning

confidence: 99%

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

et al. 2022

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“…This document is currently used in the Cryptographic Module Validation Program (CMVP) and has been cited as a recommendation for entropy estimation in an ISO standard document ISO/ IEC-20543 (2019) for test and analysis methods of RNGs. The principles of entropy estimators in SP 800-90B have been investigated and analyzed theoretically (Kang, Park & Yeom, 2017;Zhu et al, 2017;Zhu et al, 2019). However, it is difficult to find research on the efficient implementation of the entropy estimation process of SP 800-90B.…”

Section: Introductionmentioning

confidence: 99%

Accelerated implementation for testing IID assumption of NIST SP 800-90B using GPU

Yewon

Yeom

2021

PeerJ Computer Science

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In cryptosystems and cryptographic modules, insufficient entropy of the noise sources that serve as the input into random number generator (RNG) may cause serious damage, such as compromising private keys. Therefore, it is necessary to estimate the entropy of the noise source as precisely as possible. The National Institute of Standards and Technology (NIST) published a standard document known as Special Publication (SP) 800-90B, which describes the method for estimating the entropy of the noise source that is the input into an RNG. The NIST offers two programs for running the entropy estimation process of SP 800-90B, which are written in Python and C++. The running time for estimating the entropy is more than one hour for each noise source. An RNG tends to use several noise sources in each operating system supported, and the noise sources are affected by the environment. Therefore, the NIST program should be run several times to analyze the security of RNG. The NIST estimation runtimes are a burden for developers as well as evaluators working for the Cryptographic Module Validation Program. In this study, we propose a GPU-based parallel implementation of the most time-consuming part of the entropy estimation, namely the independent and identically distributed (IID) assumption testing process. To achieve maximal GPU performance, we propose a scalable method that adjusts the optimal size of the global memory allocations depending on GPU capability and balances the workload between streaming multiprocessors. Our GPU-based implementation excluded one statistical test, which is not suitable for GPU implementation. We propose a hybrid CPU/GPU implementation that consists of our GPU-based program and the excluded statistical test that runs using OpenMP. The experimental results demonstrate that our method is about 3 to 25 times faster than that of the NIST package.

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Designing hardware for a robust high-speed cryptographic key generator based on multiple chaotic systems and its FPGA implementation for real-time video encryption

İnce,

Karakaya,

Türk

2024

Multimed Tools Appl

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Recent advancements in communication technologies have highlighted the pivotal role of information security for all individuals and entities. In response, researchers are increasingly focusing on cryptographic solutions to ensure the reliability of confidential information. Recognizing the superiority of chaotic systems preference as entropy source of cryptographic systems, this paper proposes a novel true random number generator (TRNG) design by combining four different chaotic systems outputs, tailored for real-time video encryption application. These chaotic systems are continuous-time Lorenz and fractional-order Chen-Lee systems, as well as discrete-time Logistic and Tent maps. This study generates true random bit (TRB) sequences at a high bit rate (25 Mbps) through the hardware implementations of four distinct chaotic systems to have the best statistical randomness in the resulting output. Then, the cryptographic true random key bits (8-bit at 25 MHz frequency) are employed in the post-processing with real-time video data by using the XOR operation, a fundamental post-processing algorithm. The real-time video encryption application is executed on an experimental assembly, composed of a Field Programmable Gate Array (FPGA) development kit, an OV7670 camera module, a VGA monitor, and prototype circuit boards for the chaotic systems. To evaluate the effectiveness of the proposed encryption system, several security assessments are conducted. These include NIST SP 800 − 22 statistical tests, FIPS 140-1 standards, chi-square tests, histogram and correlation analysis, and NPCR and UACI differential attack resilience tests. Consequently, the findings suggest that the presented real-time embedded cryptosystem is robust and suitable for secure communications, particularly in the realm of video transmission.

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On the Additional Chi-Square Tests for the IID Assumption of NIST SP 800-90B

Cited by 3 publications

References 8 publications

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

Accelerated implementation for testing IID assumption of NIST SP 800-90B using GPU

Designing hardware for a robust high-speed cryptographic key generator based on multiple chaotic systems and its FPGA implementation for real-time video encryption

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