Integrating Remote Quantum Random Number Generator as a Shared Resource into GNU/Linux via D-Bus

Data encryption is a way to increase security of data by using cryptographic keys generated from random numbers. Random Number generator (RNG), which produces such numbers, is classified as True Random Number Generator (TRNG) or Pseudorandom Number generator (PRNG). The first one uses physical phenomena to generate random numbers whereas the second one uses deterministic systems. A commonly used deterministic system in PRNG applications is the logistic map, a nonlinear system that under certain operating ranges becomes chaotic. However, the logistic map alone does not generate satisfactory random sequence because the sequence generated is not uniformly distributed, and it does not have good results in statistical tests, such as the NIST test suite. To solve this problem, this paper proposes an implementation in Field Programmable Gate Array (FPGA) of a PRNG, which uses the k – logistic map concept, discarding the most significant k decimal digits of an underlying orbit generated from the traditional logistic map equation. The results from synthesis and simulations show that the designed circuit is a low-cost and a high efficiency RNG solution.

The subject matter of the article is pseudo-random number generators. Random numbers play the important role in cryptography. Using not secure pseudo-random number generators is a very common weakness. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions but are not random, called pseudo-random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo-random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. The goal is to develop a mathematical model of the generator, which generates fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. The goal of the research is also to develop the model of a hybrid semi self-testing certification method for quantum random number generators (QRNG). The tasks to be solved are to create the mathematical model of a random number generator, which generates the fast random numbers at a lower cost. To create the mathematical model of a hybrid semi self-testing certification method for quantum random number generators. To integrate a hybrid semi self-testing certification method to the hybrid random number generator. the methods used are mathematical optimization and simulation. The following results were obtained: we present the improved hybrid quantum random number generator, which is based on QRNG, which uses the time of arrival of photons. The model of a hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the hybrid certification method is integrated into the model of the new quantum random number generator. Conclusions. The scientific novelty of the results obtained is as follows: 1. The hybrid quantum random number generator is offered, which is based on QRNG, which uses the time of the arrival of photons. It uses the simple version of the detectors with few requirements. The hybrid QRNG produces more than one random bit per the detection of each photon. It is rather efficient and has a high level of randomness. 2. The hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered. The Self-testing, as well as device-independent quantum random number generation methods, are analyzed. The advantages and disadvantages of both methods are identified. Based on the result the hybrid method is offered. 3. The hybrid semi self-testing certification method for quantum random number generators is integrated into the offered model of the quantum random number generator. The paper analyzes its security and efficiency. The paper offers to use the new random number generator in the crypto-schemes.

Random numbers are the security foundation in the field of cryptography and information security, providing the most basic security guarantee for cryptography and security protocols. The quality of random numbers depends to some extent on the randomness of the data generated by the random number generator. For devices such as mobile smart terminals, high-quality true random numbers cannot be generated because a true random number generators cannot be embedded. However, these usage scenarios have certain requirements on the quality of random numbers. A random number scheme, that is, generating higher-quality random numbers without increasing hardware investment. In this paper, a software random number generator based on the Shangmi algorithm is implemented for the situation where there is no true random number generator. The existing hardware or components are used as the entropy source, and the random information generated by the entropy source is collected as the initial data, and processed through the Shangmi algorithm. Generate a random sequence, and the final generated random number meets the Shangmi detection standard.

The basis of encryption techniques is random number generators (RNGs). The application areas of cryptology are increasing in number due to continuously developing technology, so the need for RNGs is increasing rapidly, too. RNGs can be divided into two categories as pseudorandom number generator (PRNGs) and true random number generator (TRNGs). TRNGs are systems that use unpredictable and uncontrollable entropy sources and generate random numbers. During the design of TRNGs, while analog signals belonging to the used entropy sources are being converted to digital data, generally comparators, flip-flops, Schmitt triggers, and ADCs are used. In this study, a computer-controlled new and flexible platform to find the most appropriate system parameters in ADC-based TRNG designs is designed and realized. As a sample application with this new platform, six different TRNGs that use three different outputs of Zhongtang, which is a continuous time chaotic system, as an entropy source are designed. Random number series generated with the six designed TRNGs are put through the NIST800–22 test, which has the internationally highest standards, and they pass all tests. With the help of the new platform designed, ADC-based high-quality TRNGs can be developed fast and also without the need for expertise. The platform has been designed to decide which entropy source and parameter are better by comparing them before complex embedded TRNG designs. In addition, this platform can be used for educational purposes to explain how to work an ADC-based TRNG. That is why it can be utilized as an experiment set in engineering education, as well.

Random numbers have wide-ranging applications in various domains such as cryptography, randomization of initial weights in machine learning and AI-simulation, Monte Carlo computation, industrial testing, computer games, gambling. The generation of random numbers is only possible from a sourceof entropy. A true random number generator (TRNG) uses a physical source of entropy to generate random numbers. The randomness of a TRNG can be scientifically characterized, and measured. A drawback of TRNGs is that they usually need an external hardware device containing the physical source of entropy. This necessity can be eliminated by attempting to use sources that are already part of the device's environment. This work attempts to identify such sources and analyze their entropy levels. The identified sources of entropy are then used to build a model that can be used to generate truly random numbers.

The currently proposed RISC-V True Random Number Generator (TRNG) architecture breaks with previous ISA TRNG practice by splitting the Entropy Source (ES) component away from cryptographic PRNGs into a separate interface, and in its use of polling. We describe the interface, its use in cryptography, and offer additional discussion, background, and rationale for various aspects of it. This design is informed by lessons learned from earlier mainstream ISAs, recently introduced SP 800-90B and FIPS 140-3 entropy audit requirements, AIS 31 and Common Criteria, current and emerging cryptographic needs such as post-quantum cryptography, and the goal of supporting a wide variety of RISC-V implementations and applications. Many of the architectural choices are a result of quantitative observations about random number generators in secure microcontrollers, the Linux kernel, and cryptographic libraries. We further compare the architecture to some contemporary random number generators and describe a minimalistic TRNG reference implementation that uses the Entropy Source together with RISC-V AES instructions.

In the modern world of information technology, random number generation plays a key role in many areas. The article provides a review and analysis of the possibilities of using built-in sensors of mobile devices as sources of entropy for hardware true random number generators (TRNGs). This approach allows you to reduce the cost of creating generators while maintaining a sufficient level of randomness. The object of research is the process of generating random numbers using mobile device sensors as a source of entropy. Two main types of generators are shown: software pseudo-random number generators (PRNGs), which do not always meet the unpredictability criteria, and hardware true random number generators (TRNGs), which are based on physical noise sources. To ensure high generation quality, hardware TRNGs require an effective source of entropy. It is proposed to use sensors of mobile devices - accelerometers, gyroscopes, magnetometers, barometers, light sensors, etc. They are able to register changes in the external environment or position of the device and generate large amounts of data that can be used as an entropy source. A review of previous research on the use of mobile device sensors and IoT devices is carried out. The following requirements for sensors were formulated: sensitivity, the presence of a sensor in most mobile devices, the speed of data digitization, the number of bits received per measurement. A comparative analysis of the sensors according to the specified characteristics is carried out. According to the results of the comparison, the best indicators are provided by accelerometer, gyroscope and magnetometer sensors, which led to their selection for further use as sources of entropy in hardware random number generators.

Dr Zhang Yixin of Nanjing University, China, talks about the work behind the paper ‘Portable true random number generator for personal encryption application based on smartphone camera’, page 1841. Dr Zhang Yixin My major was more related to radio communication as an undergraduate. About eight years ago, I started a PhD at Nanjing University, that was the beginning of my research in optical. In 2011, I joined NTRC at Nanyang Technological University in Singapore as a postdoctoral research fellow. The lab had just begun a joint military project on unconditional encrypted optical communication. That is where my current research work really began. Two years later I returned to Nanjing University as a lecturer and my current main field of research is single photon level optical signal detection and its applications, such as the true random number generator (TRNG) based on photon distribution. True random numbers are the key to secure communication, even if quantum technology is included. A TRNG based on an off-the-shelf image sensor, rather than a thousands-of-dollars photon counter, would be much more attractive for portable personal encryption applications, such as E-payment, privacy call and cryptographic data transmission on a smartphone or laptop. In the near future, the security level of personal devices has to be enhanced with new encryption technology to stand against the technological advances of eavesdroppers; this is the goal of our current project. Present TRNGs are mainly based on specialised, expensive hardware like single photon detectors, chaotic lasers and radioactive nuclei, which are more suited to commercial or academic applications than personal usage. We present a portable TRNG configuration simply based on the camera of a smartphone. The randomness of the output bit sequence has been proved with NIST tests, and all necessary processing functions could be fully integrated within Android software in the near future. This approach offers a promising solution for portable personal encryption, since no equipment is needed except the smartphone itself. Back in my PhD days, working on micro-structure imaging, our group confirmed by experiment that in certain conditions, shot noise other than thermal noise can dominate the overall noise characteristics of a high-sensitivity CCD image sensor. Commonly speaking, shot noise of photocurrent is believed to be a quantum process and true random number could be generated accordingly. However, the performance of image sensors on smartphones were not as good then. Two or three years later, I saw a picture taken by my colleague's latest phone, and thought maybe it was time to realise the portable TRNG with smartphone camera. I see two main trends in recent TRNG research. The first is realising higher bit rates; speeds of gigabits per second were reported several years ago. The second is looking for new physical random phenomena that are more reliable. Arguments have always existed on whether chaotic process is as random as quantum process. However, I believe that the integration of TRNGs is very likely to be the main topic in the future. TRNGs are still too big and expensive for most practical applications. Integration of optics and electronics components on a single chip is necessary for portable and low-cost TRNGs. Meanwhile, TRNGs only solve the random number generation problem, a communication protocol which employs true random numbers as secret keys for encryption also needs to be developed. Here at the Institute of Optical Communication Engineering at Nanjing, my group will try to improve the Android App for TRNGs to automatically adapt to different types of smartphone, since the camera setting is essential to the overall performance of output random bits. We are also working on high-speed single photon counting technology based on avalanche photodiodes to improve the detection speed if higher rates are required. Another research area we are involved in is fibre optical sensing, with the goal of locating eavesdroppers in time, while they are intercepting optical signals from fibre communication links. Probe pulse coding based on TRNGs will be used for the improvement of measuring speed, spatial resolution and dynamic range.

Random numbers are at the heart of diverse fields, ranging from simulations of stochastic processes to classical and quantum cryptography. The requirement for true randomness in these applications has motivated various proposals for generating random numbers based on the inherent randomness of quantum systems. The generation of true random numbers with arbitrarily defined probability distributions is highly desirable for applications, but it is very challenging. Here we show that single-photon quantum walks can generate multi-bit random numbers with on-demand probability distributions, when the required “coin” parameters are found with the gradient descent (GD) algorithm. Our theoretical and experimental results exhibit high fidelity for various selected distributions. This GD-enhanced single-photon system provides a convenient way for building flexible and reliable quantum random number generators. Multi-bit random numbers are a necessary resource for high-dimensional quantum key distribution.

True random number generator (TRNG) is the important hardware security primitive for modern internet of things (IoT) devices, while the entropy source (ES) serves as the most crucial component for TRNG. This paper explores the NAND-XOR ring oscillators (NXROs) structure to design a novel ES architecture for TRNG. The basic principle of the ES is to add a self-feedback NAND gate in XOR RO to generate a high-frequency signal, so as to apply the signal to induce a high-frequency change of XOR RO oscillation states and thus to achieve a more significant amplification for random clock jitter. In addition, our NXRO has a higher oscillation frequency and min-entropy than traditional ROs. We implement a TRNG with the new NXRO ES unit on both Xilinx Spartan-6 and Atrix-7 FPGAs. Our experiment results demonstrate that compared with the state-of-the-art TRNGs, the new TRNG achieves a higher throughput and lower hardware overhead in generating true random numbers successfully passing the NIST test and AIS31 test without post-processing.

A true random number generator (TRNG) is suitable for generating secure keys and nonces. TRNGs implemented in IoT devices must be small in scale and have low power consumption. The random number sequence generated by TRNG needs to have high entropy immediately after startup and a stable state. In this paper, three types of ring oscillator type TRNGs, TERO-based, COSO-based, and STR-based TRNG, are implemented on Zynq-7010. When these TRNGs are implemented as a single entropy source, it is challenging to implement them because it is necessary to evaluate the layout and wiring for each FPGA. This paper proposes a TRNG configuration, which exclusively ORs the outputs of multiple entropy sources. We show that this configuration can reduce the implementing difficulty and realize high entropy. For the random number sequence evaluation, we use the statistical test of NIST SP800-90B and BSI AIS 20/31. In addition, the random number sequence immediately after the startup is also statistically evaluated. As a result, our proposed TRNGs generate high entropy random numbers and are easy to implement on FPGA when we implement TRNGs with eight single entropy sources for TERO-based TRNG, 48 for COSO-based TRNG, and two for STR-based TRNG, respectively.

The triboelectric nanogenerator (TENG) has the potential to serve as a high-entropy energy harvester, enabling the self-powered operation of Internet of Things (IoT) devices. True random number generator (TRNG) is a common feature of encryption used in IoT data communication, ensuring the security of transmitted information. The benefits of multiplexing TENG and TRNG in resource-constrained IoT devices are substantial. However, current designs are limited by the usage scenarios and throughput of the TRNG. Specifically, we propose a structurally and environmentally friendly design based on the contact-separation structure, integrating heat fluctuation and charge decay as entropy sources. Furthermore, filtering and differential algorithms are recommended for data processing based on TENG characteristics to enhance randomness. Finally, a TENG-based TRNG is fabricated, and its performance is verified. Test results demonstrate a random number throughput of 25 Mbps with a randomness test pass rate approaching 99%, demonstrating suitability for resource-constrained IoT applications.

In the rapidly developing Internet of Things (IoT) applications, how to achieve rapid identification of massive devices and secure the communication of wireless data based on low cost and low power consumption is the key problem to be solved urgently. This paper proposes a novel true random number generator (TRNG) based on ADC nonlinear effect and chaotic map, which can be implemented by traditional processors with built-in ADCs, such as MCU, DSP, ARM, and FPGA. The processor controls the ADC to sample the changing input signal to obtain the digital signal DADC and then extracts some bits of DADC to generate the true random number (TRN). At the same time, after a delay based on DADC, the next time ADC sampling is carried out, and the cycle continues until the processor stops generating the TRN. Due to the nonlinear effect of ADC, the DADC obtained from each sampling is stochastic, and the changing input signal will sharply change the delay time, thus changing the sampling interval (called random interval sampling). As the input signal changes, DADC with strong randomness is obtained. The whole operation of the TRNG resembles a chaotic map, and this method also eliminates the pseudorandom property of chaotic map by combining the variable input signal (including noise) with the nonlinear effect of ADC. The simulation and actual test data are verified by NIST, and the verification results show that the random numbers generated by the proposed method have strong randomness and can be used to implement TRNG. The proposed TRNG has the advantages of low cost, low power consumption, and strong compatibility, and the rate of generating true random number is more than 1.6 Mbps (determined by ADC sampling rate and processor frequency), which is very suitable for IoT sensor devices for security encryption algorithms and anticollision.

True random number generation (TRNG) is one of the prominent research areas in present scenario of cryptography and security. It has been reported in the recent past that even TRNG encounters security threats. In order to ensure the security of the random numbers, entropy of random numbers being generated should be high. There are different approaches to generate the random numbers from the physical processes, ranging from jitter to chaos. Various schemes employing the jitter as entropy source have been reported. The usage of jitter in ring oscillator aids in obtaining a high speed real-time random number generation (RNG). On the other hand, the asynchronous architecture ensures high security, which has been implemented in the work. Re-configuring these two architectures develops a RNG with high-speed and security. The statistical tests along with internal tests are conducted to ensure security in the architecture. National Institute of Standards and Technology (NIST) tests validated the unpredictability and randomness of the true random number (TRN) generated.

True random number generators (TRNG) play an important role in many fields that require unpredictable and nondeterministic random number sequences. Unlike their pseudorandom counterparts, TRNGs are more computationally expensive as they need to harvest entropy from physical phenomena. To generate high quality true random numbers at a fast rate, this paper introduces a new TRNG based on hyperchaos and digital sound. The characteristics of a hyperchaotic map such as sensitivity to initial conditions and complex behavior amplifies noise obtained by sampling environmental sound through a computer microphone. The random numbers generated are then evaluated using statistical test suites such as NIST SP 800-22, DIEHARD and ENT. Because nondeterminism cannot be proved by merely running test suites, entropy analysis is performed to determine the unpredictability of these sequences. Results show that the proposed TRNG can generate true random numbers at a high rate while maintaining strong statistical quality. In addition, the entropy source requires only an inexpensive computer microphone which is already built into many laptops and handheld devices. Therefore, the proposed generator provides a low-cost and easily obtainable option for applications that require true random numbers.