The report investigates the physical nature of entropy sources used to build hardware true random number generators (TRNG). The thermodynamic fluctuations of electrons (resistor thermal noise) and hardware micro-noises of electronic components are analyzed as a fundamental basis for forming absolutely unpredictable sequences. A comparison is made between generators based on stochastic physical processes and mathematical models simulating randomness (additive Fibonacci generators).
The report investigates the physical nature of entropy sources used to build hardware true random number generators (TRNG). The thermodynamic fluctuations of electrons (resistor thermal noise) and hardware micro-noises of electronic components are analyzed as a fundamental basis for forming absolutely unpredictable sequences. A comparison is made between generators based on stochastic physical processes and mathematical models simulating randomness (additive Fibonacci generators).
As technologies such as artificial intelligence, autonomous driving, the Internet of Things, wearable electronics, and edge computing continue to spread in the era of the Fourth Industrial Revolution, the importance of hardware security for the safe storage, transmission, and processing of large volumes of data has grown substantially. One of the key components of such security systems is the true random number generator (TRNG), which produces unpredictable random numbers for cryptographic use. In recent years, research on TRNGs has increasingly moved beyond conventional CMOS-based approaches toward semiconductor devices built from emerging materials. These material-based TRNGs offer several advantages, including high integration density, low power consumption, compact form factors, and strong suitability for next-generation edge and IoT environments, because they can directly exploit the intrinsic stochasticity of the device itself as an entropy source. In this review, recent studies on TRNGs based on emerging material-based semiconductor devices are examined from the perspectives of entropy sources, device structures, randomness validation, and wearable/flexible extensions. By bringing together the key physical mechanisms, device platforms, evaluation criteria, and prospects for wearable and flexible electronics in edge and IoT environments, this review aims to provide a useful framework for future research on hardware security devices.
Random numbers play a central role in cryptography, cyber security, and gaming applications. Extracting bits from some random natural activity is one of the primitive ways to generate random numbers. True random number generator(TRNG) and quantum random number generator(QRNG) are examples of this random number sequence. The development of quantum technology made it possible to extract random sequences from quantum entropy sources. This sequence is called quantum random numbers and the whole setup is named a quantum random number generator. A suitable sampling and a physical device for the entropy source are the building blocks for a TRNG. Here we present a comparative analysis of two random number generators. Apart from the conventional statistical test for randomness, here we introduce a straightforward coefficient based on the concept of non-linearity of the Boolean function to determine the availability of any linearity in a chosen random binary sequence. It reveals the cryptographic suitability of the generated sequence w.r.t any correlation attack. A comparison of the nonlinearity of two Boolean sequences generated from two different random sources is presented.
Random numbers are important in diverse areas of science and technology. The requirements of randomness, however, are more stringent in cryptographic applications, where so-called true randomness is required. In particular, statistical randomness is not enough, and a degree of unpredictability stemmed in physical phenomenon is used to build a true random number generator (TRNG). A critical part in TRNG design is the entropy harvester, which extracts entropy from multiple entropy sources. Unfortunately, none of such sources provide true randomness in pure form, and the desired signal is heavily contaminated due to electrical coupling, electromagnetic interference, and other pseudo-random signals present in physical environment. Process variation and ageing affect the performance of noise sources. In this paper we propose an entropy extraction and estimation mechanism, which operates directly on raw randomness exhibited by analogue noise sources. The main strength of our design is its dynamic nature, i.e., it adapts as per the current state of source, and provides a robust mechanism to detect entropy failure. Our design can easily be implemented on commodity hardware in a cost effective manner.
Generators of random sequences used in high-end applications such as cryptography rely on entropy sources for their indeterminism. Physical processes governed by the laws of quantum mechanics are excellent sources of entropy available in nature. However, extracting enough entropy from such systems for generating truly random sequences is challenging while maintaining the feasibility of the extraction procedure for real-world applications. Here, we present a compact and an all-electronic van der Waals heterostructure-based device capable of detecting discrete charge fluctuations for extracting entropy from physical processes and use it for the generation of independent and identically distributed true random sequences. We extract a record-high value (>0.98 bits/bit) of min-entropy using the proposed scheme. We demonstrate an entropy generation rate tunable over multiple orders of magnitude and show the persistence of the underlying physical process for temperatures ranging from cryogenic to ambient conditions. We verify the random nature of the generated sequences using tests such as NIST SP 800-90B standard and other statistical measures and verify the suitability of our random sequence for cryptographic applications using the NIST SP 800-22 standard. The generated random sequences are then used in implementing various randomized algorithms without any preconditioning steps.
Random number generators are indispensable for a multi- tude of tasks; from electronic games to secure communications. Most gen- erators have been made either in software or determinist hardwired de- vices such as the Linear-Feedback-Shift-Registers; while gaining in costs or speed, the “random” sequences generated are actually deterministic, obeying clear generating algorithms, despite all randomness appearance of their outputs. From the other side, Nature presents a multitude of sources of true randomness that can be explored. Commercial random generators exist based on physical processes as the source of randomness. Difficulties are always present to extract Nature’s randomness. This paper presents guidelines for construction of a fast (telecommunication speed) Physical Random Number Generator. It discusses the fundamental physical ele- ments involved, technicalities of signal recording and its limitations, and the final bit extraction. The need for randomness tests is emphasized and the impossibility of guaranteeing true randomness of a finite sequence is discussed.
Random numbers play a crucial role in cryptography since the security of cryptographic protocols relies on the assumption of the availability of uniformly distributed and unpredictable random numbers to generate secret keys, passwords, initialization vectors, nonces, salt, etc. True Random Number Generators (TRNGs) extract random numbers from physical processes (such as radioactive decay, thermal noise, and atmospheric noise) that are inherently unpredictable. However, real-world random number generators sometimes fail and produce outputs with low entropy, leading to security vulnerabilities. It is commonly observed that the TRNG outputs have statistical biases and dependencies that make them unsuitable to be directly used for cryptographic purposes. There are some standards and guidelines on generating and testing random numbers that are suitable for cryptographic applications. The National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90 series provide guidelines and recommendations for generating random numbers for cryptographic applications and describes statistical randomness testing, estimating min-entropy with 10 black-box entropy estimation methods. In this thesis, we evaluate the effectiveness and limitations of the SP 800-90B methods by exploring the accuracy of these estimators using simulated random numbers with known entropy, investigating the correlation between entropy estimates, and studying the impacts of deterministic transformations on the estimators. To understand the unpredictability of the outputs, it is important to estimate their entropy accurately. The National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90B specifies ten min-entropy estimators ranging from simple frequency-based estimators to more advanced approaches. Each estimator has specific assumptions, making them suitable for different types of sources. The minimum of these estimates is assumed to be the min-entropy of the TRNG. We propose a new entropy estimator (estimates min-entropy and also Shannon entropy) called index-value coincidence estimate that is suitable for outputs that might include some dependencies and we also provide some experimental results that demonstrate the effectiveness of the estimator. Additionally, TRNGs may be affected by outside conditions such as temperature, humidity, etc. Health tests are an integral part of the noise source of TRNG, defined to detect unexpected changes in the working process and dramatic changes in the amount of entropy generated by the noise source. Existing health test suites are examined, and a health test suite for cryptographic TRNGs is introduced by using random variables weight, run, runs of length 1 and overlapping templates. Some suggested parameters and experimental results are given.