Randomness occurs in every kind of application in which random events should be experienced such as gambling, statistical calculations, computer simulations, information security and encryption. The quality of randomness is very important for the events using randomness. In particular, the need for random numbers used in the field of cryptology to be unpredictable increases the importance of random number generators. \n \nRandom Number Generators (RNGs) are software or hardware components that can be used to generate numbers that cannot be predicted in any pattern or relationship. Software-based RNGs are deterministic applications, while hardware-based RNGs are the devices that can be integrated into the system. \n \nEncryption using numbers generated by software-based RNG can be broken in varying times depending on the strength of the encryption because it was developed in deterministic ways. It is expected that the decoding time will be shortened considerably by the quantum computers which are tried to be developed today besides classic computers that takes a long time to analyze. The hardware RNGs are more powerful in randomness quality than software-based generators, but cost-effective and use-limited generators. \n \nQuantum Random Number Generators (QRNGs) is a kind of generator based on quantum physics laws instead of classical physics. In photonic based QRNGs are produced the random numbers after various software and hardware operations by taking advantage of the uncertainty of photons. These generated numbers are unpredictably strong random numbers. \n \nIn this study, a QRNG was obtained by using the RGB (Red-Green-Blue) values of the images taken from a white-single LED light source and the randomness tests of the produced numbers were carried out with the tests provided by NIST (National Institute of Standards and Technology). As a result, the numbers derived from the images successfully passed NIST tests and true random number sequences were obtained.
Randomness occurs in every kind of application in which random events should be experienced such as gambling, statistical calculations, computer simulations, information security and encryption. The quality of randomness is very important for the events using randomness. In particular, the need for random numbers used in the field of cryptology to be unpredictable increases the importance of random number generators. \n \nRandom Number Generators (RNGs) are software or hardware components that can be used to generate numbers that cannot be predicted in any pattern or relationship. Software-based RNGs are deterministic applications, while hardware-based RNGs are the devices that can be integrated into the system. \n \nEncryption using numbers generated by software-based RNG can be broken in varying times depending on the strength of the encryption because it was developed in deterministic ways. It is expected that the decoding time will be shortened considerably by the quantum computers which are tried to be developed today besides classic computers that takes a long time to analyze. The hardware RNGs are more powerful in randomness quality than software-based generators, but cost-effective and use-limited generators. \n \nQuantum Random Number Generators (QRNGs) is a kind of generator based on quantum physics laws instead of classical physics. In photonic based QRNGs are produced the random numbers after various software and hardware operations by taking advantage of the uncertainty of photons. These generated numbers are unpredictably strong random numbers. \n \nIn this study, a QRNG was obtained by using the RGB (Red-Green-Blue) values of the images taken from a white-single LED light source and the randomness tests of the produced numbers were carried out with the tests provided by NIST (National Institute of Standards and Technology). As a result, the numbers derived from the images successfully passed NIST tests and true random number sequences were obtained.
Random numbers are fundamental elements in different fields of science and technology such as computer simulation like Monte Carlo-method simulation, statistical sampling, cryptography, games and gambling, and other areas where unpredictable results are necessary. Random number generators (RNG) are generally classified as “pseudo”-random number generators (PRNG) and "truly" random number generators (TRNG). Pseudo random numbers are generated by computer algorithms with a (random) seed and a specific formula. The random numbers produced in this way (with a small degree of unpredictability) are good enough for some applications such as computer simulation. However, for some other applications like cryptography they are not completely reliable. When the seed is revealed, the entire sequence of numbers can be produced. The periodicity is also an undesirable property of PRNGs that can be disregarded for most practical purposes if the sequence recurs after a very long period. However, the predictability still remains a tremendous disadvantage of this type of generators. Truly random numbers, on the other hand, can be generated through physical sources of randomness like flipping a coin. However, the approaches exploiting classical motion and classical physics to generate random numbers possess a deterministic nature that is transferred to the generated random numbers. The best solution is to benefit from the assets of indeterminacy and randomness in quantum physics. Based on the quantum theory, the properties of a particle cannot be determined with arbitrary precision until a measurement is carried out. The result of a measurement, therefore, remains unpredictable and random. Optical phenomena including photons as the quanta of light have various random, non-deterministic properties. These properties include the polarization of the photons, the exact number of photons impinging a detector and the photon arrival times. Such intrinsically random properties can be exploited to generate truly random numbers. Silicon (Si) is considered as an interesting material in integrated optics. Microelectronic chips made from Si are cheap and easy to mass-fabricate, and can be densely integrated. Si integrated optical chips, that can generate, modulate, process and detect light signals, exploit the benefits of Si while also being fully compatible with electronic. Since many electronic components can be integrated into a single chip, Si is an ideal candidate for the production of small, powerful devices. By complementary metal-oxide-semiconductor (CMOS) technology, the fabrication of compact and mass manufacturable devices with integrated components on the Si platform is achievable. In this thesis we aim to model, study and fabricate a compact photonic quantum random number generator (QRNG) on the Si platform that is able to generate high quality, "truly" random numbers. The proposed QRNG is based on a Si light source (LED) coupled with a Si single photon avalanche diode (SPAD) or an array of SPADs which is called Si photomultiplier (SiPM). Various implementations of QRNG have been developed reaching an ultimate geometry where both the source and the SPAD are integrated on the same chip and fabricated by the same process. This activity was performed within the project SiQuro—on Si chip quantum optics for quantum computing and secure communications—which aims to bring the quantum world into integrated photonics. By using the same successful paradigm of microelectronics—the study and design of very small electronic devices typically made from semiconductor materials—, the vision is to have low cost and mass manufacturable integrated quantum photonic circuits for a variety of different applications in quantum computing, measure, sensing, secure communications and services. The Si platform permits, in a natural way, the integration of quantum photonics with electronics. Two methodologies are presented to generate random numbers: one is based on photon counting measurements and another one is based on photon arrival time measurements. The latter is robust, masks all the drawbacks of afterpulsing, dead time and jitter of the Si SPAD and is effectively insensitive to ageing of the LED and to its emission drifts related to temperature variations. The raw data pass all the statistical tests in national institute of standards and technology (NIST) tests suite and TestU01 Alphabit battery without a post processing algorithm. The maximum demonstrated bit rate is 1.68 Mbps with the efficiency of 4-bits per detected photon. In order to realize a small, portable QRNG, we have produced a compact configuration consisting of a Si nanocrystals (Si-NCs) LED and a SiPM. All the statistical test in the NIST tests suite pass for the raw data with the maximum bit rate of 0.5 Mbps. We also prepared and studied a compact chip consisting of a Si-NCs LED and an array of detectors. An integrated chip, composed of Si p+/n junction working in avalanche region and a Si SPAD, was produced as well. High quality random numbers are produced through our robust methodology at the highest speed of 100 kcps. Integration of the source of entropy and the detector on a single chip is an efficient way to produce a compact RNG. A small RNG is an essential element to guarantee the security of our everyday life. It can be readily implemented into electronic devices for data encryption. The idea of "utmost security" would no longer be limited to particular organs owning sensitive information. It would be accessible to every one in everyday life.
Even if the output of a Random Number Generator (RNG) is perfectly uniformly distributed, it may be correlated to pre-existing information and therefore be predictable. Statistical tests are thus not sufficient to guarantee that an RNG is usable for applications, e.g., in cryptography or gambling, where unpredictability is important. To enable such applications a stronger notion of randomness, termed "true randomness", is required, which includes independence from prior information. Quantum systems are particularly suitable for true randomness generation, as their unpredictability can be proved based on physical principles. Practical implementations of Quantum RNGs (QRNGs) are however always subject to noise, i.e., influences which are not fully controlled. This reduces the quality of the raw randomness generated by the device, making it necessary to post-process it. Here we provide a framework to analyse realistic QRNGs and to determine the post-processing that is necessary to turn their raw output into true randomness.
Random number generators (RNG) are an important resource in many areas: cryptography (both quantum and classical), probabilistic computation (Monte Carlo methods), numerical simulations, industrial testing and labeling, hazard games, scientific research etc. Because today's computers are deterministic, they can not create random numbers unless complemented with a physical RNG. Randomness of a RNG can be defined and scientifically characterized and measured. Especially valuable is the information-theoretic provable RNG which, at state of the art, seem to be possible only by harvest of randomness inherent to certain (simple) quantum systems and such a generator we call Quantum RNG (QRNG). On the other hand, current industry standards dictate use of RNGs based on free running oscillators (FRO) whose randomness is derived from electronics noise present in logic circuits and which, although quantum in nature, cannot be strictly proven. This approach is currently used in FPGA and ASIC chips. We compare weak and strong aspects of the two approaches for use in cryptography and in general. We also give an alternative definition of randomness, discuss usage of single photon detectors in realization of QRNGs and give several examples where QRNG can significantly improve security of a cryptographic system.
Random Number Generators are software or hardware components that allow the production of unpredictable number sequences without any pattern or relationship between them. Various studies have been conducted with different techniques regarding RNG. In these studies, the difficulties of random number generation and the high cost negatively affect the efficiency of the developed generators. Many different methods have been used in real random number generation, and even quantum random number generators have been developed to make predictability difficult. Quantum Random Number Generators; are a tepe of generator based on the laws of Quantum physics instead of classical physics. In photonic-based RNG, random numbers are generated after various software and hardware operations by utilizing the uncertainty of photons. This study, it is aimed to develop a true random number generator using chemical reactions that have not been studied before. Data was produced by using sensors and other hardware elements together, the values produced were taken as seed values and assigned as input to the algorithm used in generating random numbers, and true random numbers were produced and these numbers were tested in detail with known test methods.
Random Number Generators (RNGs) are used in various applications, including cryptography, scientific simulations, gaming, and virtual reality. The quality of these random numbers is crucial, especially for computational approaches that rely on unpredictability to produce accurate results. In this thesis, we perform a comprehensive assessment of the quality of a novel Quantum Random Number Generator (QRNG) based on single photon. QRNGs leverage quantum mechanical properties to produce truly random numbers. This ensures high entropy, unpredictability, and security, making QRNGs particularly useful in fields that require high-quality randomness.We evaluate the statistical properties of strings generated by our QRNG and then its practical application by comparing it to traditional Pseudo-Random Number Generators (PRNGs) in Monte Carlo Integration (MCI), a technique that estimates integrals using random sampling. In particular, we use two statistical randomness test suits, namely NIST and Dieharder, to assess the statistical properties of our QRNG. All tests within these tests were passed, confirming that the corresponding QRNG achieves true randomness with sufficient high entropy. Next, we use our QRNG-generated numbers to seed Monte Carlo Integration (MCI) and compare it against results obtained using classical PRNG. The QRNG-based MCI demonstrated more accurate and stable numerical estimates, showing lower error rates and improved convergence compared to PRNG-based simulations.These findings highlight the advantages of QRNGs in enhancing numerical accuracy and computational security. While QRNGs may have slower generation rates than PRNGs, their superior randomness quality allows for more reliable Monte Carlo-based simulations, suggesting that fewer samples may be needed to achieve the same level of accuracy compared to classical PRNGs. This research underscores the potential of QRNG-powered numerical methods, paving the way for their integration into more advanced applications, including scientific computing, financial modeling, and cryptographic security.
Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.