Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.
Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.
Abstract Random numbers are critical to information security. Quantum random numbers are theoretically truly random and unpredictable. However, the measurement process of a quantum random number generator (QRNG) can be affected by environmental disturbances that compromise the integrity of the generated random numbers. A machine learning model is proposed to assess the stochasticity of the continuous variable QRNG under the influence of vacuum noise. The model is designed to detect the correlation between the randomness of the QRNG being corrupted under the influence of classical noise (electrical noise). In addition, our model detects a decrease in the randomness of the QRNG random numbers when the electrical noise intensity increases to a certain correlation. The results show that machine learning (ML) can be used as a measure of the quality of QRNG.
The random number generator (RNG) plays an essential role in lots of applications such as cryptographic. Unlike classical random number generators, quantum random number generators (QRNG) can in principle generate true random numbers because it is based on the inherent uncertainty of the quantum property. However, the presence of the environmental noise or the imperfect characteristics of the device can compromise the integrity of QRNG. To judge the quality of QRNG, we propose to apply the transformer based machine learning (ML) model to quantify the randomness of quantum random numbers. Through proper training, our transformer based ML model is able to predict randomness of given quantum random number sequence very closely to the maximum guess probability of its theoretical limit. Moreover, we also do comparisons between our present transformer based model with other ML model, such as the long short term memory network (LSTM) based model. Simulation results show that our present model can not only present a higher prediction accuracy, but also cost much less time and exhibit excellent performance in work efficiency. Furthermore, we also compare our present transformer based machine learning model with the traditional randomness detection standard, NIST-test-suite, and show advantages of our method.
Random prime number generation is crucial for the implementation of several encryption and signature protocols in cryptographic applications. Recently, Quantum Random Number Generators (QRNGs) have emerged as a solution to obtain information-theoretically secure randomness, setting them apart from algorithmic generators. Nonetheless, their output is still prone to deterioration under a flawed implementation and thus requires continuous statistical validation. This process is uniquely challenging for prime number generators since they cannot be submitted to the traditional testing suites, which are primarily designed to test uniform pseudorandom sources. Here, we enable direct statistical testing of prime Random Number Generators (RNGs) by extending a validation framework based on an equiprobable binning of the prime distribution with a predictive machine learning model, which can independently learn correlations in the prime output. The validity of this framework is then verified through extensive cryptanalysis of a prime QRNG based on quadrature measurements of the vacuum state. The developed model successfully identifies flawed configurations of the prime QRNG for output lengths up to 128 bits. This assessment was additionally extended to a purely classical scheme derived from electronic noise measurements. Although no increase in the model’s predictive capacity is seen, our testing framework can nonetheless demonstrate that this classical source produces a conclusively biased prime distribution. In turn, the implemented QRNG remains resilient against this type of cryptanalysis.
The quantum random number generator (QRNG) is theoretically capable of generating unpredictable random numbers based on the inherent uncertainty of quantum mechanics, which is of paramount importance for information security. However, the security of practical QRNG is susceptible to the influence of unknown classical noise introduced by imperfect measurement devices, leading to potential security threats. In this paper, we propose a deep learning-based prediction model for analyzing the bidirectional security of QRNG where the quantum source is contaminated by classical noise. Firstly, we train a deep learning model capable of evaluating the randomness of mixed entropy source composed of quantum source and classical source, which exhibits excellent performance and effectively avoids the limitations of Statistical tests in evaluating the randomness of the mixture of quantum and classical source results. Secondly, systematically analyzes the impact of non-ideal measurement devices on the practical security of the continuous variable QRNG, which provides an explicit basis for compensating the discrepancy between theory and experiment. Finally, we perform correlation detection on QRNG output sequences with a deep learning model and focus on both the forward and backward security of random numbers. Through bidirectional security detection, random number sequences that may be biased or manipulated can be more accurately and comprehensively evaluated, further preventing potential correlations from opening security holes for eavesdroppers.
Randomness is essential in cryptography for key generation and secure information exchange, ensuring confidentiality and protection. Quantum Random Number Generators (QRNGs) leverage quantum mechanics to produce truly random numbers, offering a major advantage over Pseudo Random Number Generators (PRNGs), which are slower and less secure due to their deterministic nature. Quantum computing boosts QRNG efficiency, making them ideal for cryptographic applications, such as secure key exchange and encrypted communication. QRNGs generate stronger encryption systems by utilizing quantum phenomena like Bloch sphere rotations. The integration of machine learning (ML) with QRNGs further enhances their effectiveness, optimizing random sequences for improved speed and security. This combination of QRNGs and ML provides a powerful approach to cryptography, ensuring more secure communication channels and better protection against potential attacks.
Abstract As a fundamental phenomenon in nature, randomness has a wide range of applications in the fields of science and engineering. Among different types of random number generators (RNG), quantum random number generator (QRNG) is a kind of promising RNG as it can provide provable true random numbers based on the inherent randomness of fundamental quantum processes. Nevertheless, the randomness from a QRNG can be diminished (or even destroyed) if the devices (especially the entropy source devices) are not perfect or ill-characterized. To eliminate the practical security loopholes from the source, source-independent QRNGs, which allow the source to have arbitrary and unknown dimensions, have been introduced and become one of the most important semi-device-independent (DI) QRNGs. Herein a method that enables ultra-fast unpredictable quantum random number generation from quadrature fluctuations of quantum optical field without any assumptions on the input states is proposed. Particularly, to estimate a lower bound on the extractable randomness that is independent from side information held by an eavesdropper, a new security analysis framework is established based on the extremality of Gaussian states, which can be easily extended to design and analyze new semi-DI continuous variable QRNG protocols. Moreover, the practical imperfections of the QRNG including the effects of excess noise, finite sampling range, finite resolution and asymmetric conjugate quadratures are taken into account and quantitatively analyzed. Finally, the proposed method is experimentally demonstrated to obtain high secure random number generation rates of 15.07 Gbits s −1 in off-line configuration and can potentially achieve 6 Gbits s −1 by real-time post-processing.