A comprehensive performance analysis of a TRNG system that is based on beat frequency is provided in this paper for secure, high-entropy applications. The proposed method generates unpredictable bit streams by leveraging the intrinsic unpredictability caused by the frequency difference between two distinct oscillators. The system employs phase noise and variations in the surrounding environment to increase entropy without the need for laborious post-processing procedures. The study evaluates statistical variance using well-known test suites such as the NIST and Diehard tests to ensure high-quality output. The entropy rate, stability, power consumption, and throughput are among the critical performance metrics that are pertinent. The experimental data firmly refutes exogenous influences and deterministic patterns.
A comprehensive performance analysis of a TRNG system that is based on beat frequency is provided in this paper for secure, high-entropy applications. The proposed method generates unpredictable bit streams by leveraging the intrinsic unpredictability caused by the frequency difference between two distinct oscillators. The system employs phase noise and variations in the surrounding environment to increase entropy without the need for laborious post-processing procedures. The study evaluates statistical variance using well-known test suites such as the NIST and Diehard tests to ensure high-quality output. The entropy rate, stability, power consumption, and throughput are among the critical performance metrics that are pertinent. The experimental data firmly refutes exogenous influences and deterministic patterns.
Generating random bit streams is required in various applications, most notably in cyber-security, which is essential for Internet of Everything applications to enable secure communication between interconnected devices. Ensuring high-quality and robust randomness is crucial to mitigate risks associated with predictability and system compromise. True random numbers provide the highest levels of unpredictability. However, known systematic biases that can emerge from physical imperfections, environmental variations, and device aging in the processes exploited for random number generation must be carefully monitored. This article reports the implementation and characterization of an online procedure for the detection of anomalies in a true random bit stream. It is based on the NIST adaptive proportion and repetition count tests, complemented by statistical analysis relying on the Monobit and RUNS tests. The procedure is implemented in firmware through dedicated hardware accelerators processing configurable-length sequences, with automated anomaly detection triggering alerts after three consecutive threshold violations. The implementation is performed simultaneously with bit stream generation and also provides an estimate of the entropy of the source. A statistical analysis of the results from the NIST procedure to evaluate the symbols of the bit-stream as independently and identically distributed is also performed, leading to a computation of the minimum entropy of the source that cross-checks the previously mentioned estimate. The experimental validation of the approach is performed up the bit streams generated by a quantum, silicon-based entropy source.
Random Number Generators (RNGs) are crucial for applications ranging from cryptography to simulations. Depending on the source of randomness, RNGs are classified into Pseudo-Random Number Generators (PRNGs), True Random Number Generators (TRNGs), and Quantum Random Number Generators (QRNGs). This work presents the end-to-end development of a high-speed, high-efficiency, phase-noise-based QRNG system that taps into the quantum phase noise of a single-frequency laser, with randomness originating from spontaneous emission. Using a self-heterodyne measurement with a semiconductor laser (linewidth ≈ 5.23 GHz) operated near threshold and a $\sim$48 cm fiber delay line, a raw data generation rate of 2.0 Gbps is achieved. To ensure uniform randomness in the QRNG output, robust extraction techniques developed in-house, such as the Toeplitz Strong Extractor (TSE), are used. Randomness validation using the NIST and Diehard test suites confirms that all statistical tests pass at standard confidence levels. The developed system achieves a post-processed generation rate of 1.0 Gbps in operation and attains a Technology Readiness Level (TRL) of 7, approaching TRL 8, making it suitable for real-time secure applications such as cryptographic key generation and stochastic modeling.
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.
Secure data transmission depends on unpredictable cryptographic keys. Conventional pseudorandom number generators are deterministic and vulnerable to prediction. This work proposes a Quantum True Random Number Generator (Q-TRNG) that uses quantum superposition and noise to produce truly random numbers. The generated quantum entropy is converted into high-quality random bits and applied to secure encryption, improving confidentiality and resistance to attacks. The proposed system offers a reliable and future- ready solution for quantum-grade secure communication.
Radio noise use in True Random Number Generation is a popular and well understood strategy, but relatively undermined in research. Given that Very Low Frequency radio streams record chaotic weather events from around the world (like thunderstorms), this novel entropy source sparked our interest. Our choice is supported by prior research in statistical modeling, along with an appropriate combination of both signal and statistical methods. Hence, we describe in detail our processing pipeline for engineering a TRNG from VLF radio noise. It yields high entropy, unbiased and identically distributed outputs that pass FIPS 140-2 tests, regardless of the chosen VLF stream source. To strengthen reproducibility, we have based all of our experiments on publicly available data. We also provide an open-source, modular command-line tool for building stream-based processing pipelines. Furthermore, we offer an example implementing a TRNG from FM radio noise found between standard transmission bands.
This study presents a new approach to generating true random numbers by exploiting the random nature inherent in mobile communication networks. Taking advantage of fluctuations in total users, user activity rate, and channel fading, we receive a raw signal in the radio spectrum using bowtie microstrip antennas designed for GSM and 5G frequency bands in the 1.8 and 3.5 GHz bands that can be used in our system. The captured data is then processed using a field-programmable gate array to generate high-quality true random numbers. Bow tie antennas are designed to have an omnidirectional pattern and can receive the signal from all directions. We have evaluated this through numerical calculations and antenna pattern measurements in the laboratory, and the results and their comparison show a promising performance. Our random number generation system also includes a Zynq SoC module and a dual-channel AD9226 ADC, enabling real-time random number generation through the ethernet port at up to 512Mbps. To ensure the system’s performance, standard statistical tests, including the NIST, DIEHARDER, and ENT test suites, have been done, and all confirm the true randomness of the generated random numbers. Our findings show the effectiveness of the 3.5 GHz frequency band as a suitable source for generating random numbers, especially in mobile devices and the Internet of Things. Due to the high speed of generating random numbers, this optimal and efficient TRNG system can have broad applications in cryptography, statistical analysis, and computational simulations.