Natural and artificial sequences of big data streams have various stationary and non-stationary properties. A Quantum Key Distribution (QKD) system has a quantum random number generator to protect data streams in quantum communication environments. From a cryptanalysis viewpoint, it is necessary to use statistical probability, stochastic processes and time series to evaluate quality of stationary randomness in quantum cryptographic sequrences.
Natural and artificial sequences of big data streams have various stationary and non-stationary properties. A Quantum Key Distribution (QKD) system has a quantum random number generator to protect data streams in quantum communication environments. From a cryptanalysis viewpoint, it is necessary to use statistical probability, stochastic processes and time series to evaluate quality of stationary randomness in quantum cryptographic sequrences.
Abstract In the process of quantum key distribution (QKD), the communicating parties need to randomly determine quantum states and measurement bases. To ensure the security of key distribution, we aim to use true random sequences generated by true random number generators as the source of randomness. In practical systems, due to the difficulty of obtaining true random numbers, pseudo-random number generators are used instead. Although the random numbers generated by pseudo-random number generators are statistically random, meeting the requirements of uniform distribution and independence, they rely on an initial seed to generate corresponding pseudo-random sequences. Attackers may predict future elements from the initial elements of the random sequence, posing a security risk to quantum key distribution. This paper analyzes the problems existing in current pseudo-random number generators and proposes corresponding attack methods and applicable scenarios based on the vulnerabilities in the pseudo-random sequence generation process. Under certain conditions, it is possible to obtain the keys of the communicating parties with very low error rates, thus effectively attacking the quantum key system. This paper presents new requirements for the use of random numbers in quantum key systems, which can effectively guide the security evaluation of quantum key distribution protocols.
Abstract In this chapter, a testing model is used to apply statistical probability in multiple distributions on three maps for a selected sequence to check refined stationary randomness on quantum sequences. Three random data sequences are collected from two quantum random resources: one from Australian National University (ANU) and two (initial and secure) from University of Science and Technology of China (USTC). Multiple results are created on three maps, and measurements of stationary randomness are illustrated and compared. Three samples show distinct stationary properties.
Applications of quantum technologies are rapidly progressing in communications systems implementations. In particular, quantum key distribution (QKD) and quantum random number generation have become key applications for cryptography and physical layer security. Unlike classical cryptography and network security, in QKD, security is ensured by the laws of physics and is, in principle, unbreachable by an interceptor. In particular, peculiar properties and postulates of quantum physics, such as superposition, entanglement, and quantum measurement uncertainty, are exploited to achieve system security. Quantum random number generator (QRNG) is also an important component in the implementation of QKD protocols. Likewise, in QRNG, true randomness (unpredictability using past data and uniformity of statistical distribution) is guaranteed by the laws of quantum physics. In this study, we review prominent aspects of QKD and QRNG, related standards, protocols, and hardware components. Moreover, a comprehensive review of the current QKD protocols and quantum random number generation methods is also presented.
The generation of random numbers and the study of its properties have been an elusive field for a fair portion of the century. The application of random numbers is employed in many use cases such as cryptography, neural networks, numerical simulation, and gambling. The performance of each of these use cases is profoundly impacted by the employed random numbers; henceforth, the quality of randomness is of critical importance when it comes to their usage. A poorly generated random number can make a security system vulnerable, or any numerical or statistical evaluation misleading. Although various modes of classical random number generators exist and still function to provide strong cryptographic properties, emergence of quantum mechanical randomness has shed light on a novel path of generating certified randomness which supersedes the classical counterpart in terms of security. Harnessing quantum mechanical phenomena enables generation of true random numbers which can be certified and further implemented to elevate the net quality of the specific use cases. In this work, we generate and analyze random numbers from three different sources — 50: 50 beam splitter (BS), quantum key distribution (QKD) setup with classical post-processing scheme, and a commercially available quantum random number generator (QRNG) (ID Quantique (IDQ)). The quality of the generated random numbers from the various sources is checked in statistical tests and compared. Further on, we have developed a system which implements the QRNG-based random numbers to facilitate message authentication code (MAC) and one time password (OTP) protocols, demonstrating a communication network application. In this manner we discuss about a network which integrates quantum mechanics to the current classical networking approaches to enhance certain aspects of the networking protocol — in this case, the security.
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.
Random Numbers determine the security level of cryptographic applications as they are used to generate padding schemes in the encryption/decryption process as well as used to generate cryptographic keys. This paper utilizes the QKD to generate a random quantum bit rely on BB84 protocol, using the NIST and DIEHARD randomness test algorithms to test and evaluate the randomness rates for key generation. The results show that the bits generated using QKD are truly random, which in turn, overcomes the distance limitation (associated with QKD) issue, its well-known challenges with the sending/ receiving data process between different communication parties