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Many security-related scenarios including cryptography depend on the random generation of passwords, permutations, Latin squares, CAPTCHAs and other types of non-numerical entities. Random generation of each entity type is a different problem with different solutions. This study is an attempt at a unified solution for all of the mentioned problems. This paper is the first of its kind to pose, formulate, analyze and solve the problem of random object generation as the general problem of generating random non-numerical entities. We examine solving the problem via connecting it to the well-studied random number generation problem. To this end, we highlight the challenges and propose solutions for each of them. We explain our method using a case study; random Latin square generation.

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO 2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things. Memristors can switch between high and low electrical-resistance states, but the switching behaviour can be unpredictable. Here, the authors harness this unpredictability to develop a memristor-based true random number generator that uses the stochastic delay time of threshold switching

Quantum cryptography is information-theoretically secure owing to its solid basis in quantum mechanics. However, generally, initial implementations with practical imperfections might open loopholes, allowing an eavesdropper to compromise the security of a quantum cryptographic system. This has been shown to happen for quantum key distribution (QKD). Here we apply experience from implementation security of QKD to several other quantum cryptographic primitives. We survey quantum digital signatures, quantum secret sharing, source-independent quantum random number generation, quantum secure direct communication, and blind quantum computing. We propose how the eavesdropper could in principle exploit the loopholes to violate assumptions in these protocols, breaking their security properties. Applicable countermeasures are also discussed. It is important to consider potential implementation security issues early in protocol design, to shorten the path to future applications.

In this paper, we present approaches to generating random numbers, along with potential applications. Rather than trying to provide extensive coverage of several techniques or algorithms that have appeared in the scientific literature, we focus on some representative approaches, presenting their workings and properties in detail. Our goal is to delineate their strengths and weaknesses, as well as their potential application domains, so that the reader can judge what would be the best approach for the application at hand, possibly a combination of the available approaches. For instance, a physical source of randomness can be used for the initial seed; then, suitable preprocessing can enhance its randomness; then, the output of preprocessing can feed different types of generators, e.g., a linear congruential generator, a cryptographically secure one and one based on the combination of one-way hash functions and shared key cryptoalgorithms in various modes of operation. Then, if desired, the outputs of the different generators can be combined, giving the final random sequence. Moreover, we present a set of practical randomness tests that can be applied to the outputs of random number generators in order to assess their randomness characteristics. In order to demonstrate the importance of unpredictable random sequences, we present an application of cryptographically secure generators in domains where unpredictability is one of the major requirements, i.e., eLotteries and cryptographic key generation.

Quantum technologies promise information processing and communication technology advancements, including random number generation (RNG). Using Bell inequalities, a user of a quantum RNG hardware can certify that the values provided by an untrusted device are truly random. This problem has been extensively studied for von Neumann and min-entropy as a measure of randomness. However, in this paper, we analyze the feasibility of such verification for Shannon entropy. We investigate how the usability of various Bell inequalities differs depending on the presence of noise. Moreover, we present the benefit of certification for Shannon compared to min-entropy, as well as the tight analytical lower bound for Shannon entropy in randomness certification.

Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. While statistical tests cannot definitively guarantee an RNG’s output quality, they are a powerful verification tool and the only universally applicable testing method. In this work, we design, implement, and present various post-processing methods, using randomness extractors, to improve the RNG output quality and compare them through statistical testing. We begin by performing intensive tests on three RNGs—the 32-bit linear feedback shift register (LFSR), Intel’s ‘RDSEED,’ and IDQuantique’s ‘Quantis’—and compare their performance. Next, we apply the different post-processing methods to each RNG and conduct further intensive testing on the processed output. To facilitate this, we introduce a comprehensive statistical testing environment, based on existing test suites, that can be parametrised for lightweight (fast) to intensive testing.

Substitution Box (S-Box) is one of the most significant structures used to create an encryption which is strong and resistant against attacks in block encryption algorithms. S-Box plays an important role in data encryption. This paper presents a novel S-Box generation algorithm design based on scaled Zhongtang chaotic system. In this study, a new random number generator which uses the new scaled Zhongtang chaotic system with very complicated and interesting dynamic features is designed; also, a new effective and strong S-Box design algorithm utilizing this RNG (random number generator) is developed. Bits generated by RNG are put to NIST tests and they passed all the NIST tests. Non-linearity, bit independence criteria, strict avalanche criteria, differential approximation probability performance tests are run on the proposed S-Box produced by new S-Box design algorithm. The proposed S-Box is compared with other studies available in the literature, and it is proved stronger and more effective.

The application for random numbers is ubiquitous. We experimentally build a well-studied quantum random number generator from homodyne measurements on the quadratures of the vacuum fluctuations. Semi-device-independence in this random number generator is usually obtained using phase modulators to shift the phase of the laser and obtain random sampling from both X and P quadrature measurements of the vacuum state in previous implementations. We characterize the experimental parameters for optimal performance of this source-device independent quantum random number generator by measuring the two quadratures concurrently using two homodyne detectors. We also study the influence of these parameters on randomness, which can be extracted based on Shannon entropy and von Neumann entropy, which correspond to an eavesdropper listening to classical and quantum side information, respectively.

High-quality randomness is fundamental to the security of modern cryptographic systems. We present QPP-RNG , a true random number generator (TRNG) that harvests entropy from diverse system-level jitters–including CPU pipeline timing divergences, DRAM refresh cycle perturbations, cache miss-driven memory access latencies, and other subtle hardware and operating system-induced fluctuations. QPP-RNG’s core mechanism measures the elapsed time of randomized array sorting operations–where each Fisher-Yates shuffle is infinitesimally perturbed by these microscopic jitters–and amplifies these timing variations into cryptographically strong randomness through a quantum permutation pad (QPP) architecture, all achievable on commodity hardware. The raw output of QPP-RNG underwent rigorous evaluation for independent and identically distributed (IID) behavior using the NIST SP 800-90B IID test suite, alongside the comprehensive NIST SP 800-22 and ENT statistical test batteries. Across a range of platforms, including Windows, macOS, and Raspberry Pi, QPP-RNG consistently achieved high IID min-entropy between and  bits/byte. It passed all NIST SP 800-90B IID tests with -values significantly above the threshold, confirming that its generated randomness is statistically indistinguishable from ideal IID sources derived directly from system jitter. Cross-platform analyses spanning x86_64 and ARM64 architectures further demonstrate that the extracted jitter fingerprint–and consequently the generated randomness–exhibits remarkable statistical consistency, irrespective of the underlying hardware or operating system. QPP-RNG’s entropy density compares favorably with leading commercial entropy sources. It matches or slightly exceeds the NIST IID-certified min-entropy of ID Quantique’s Quantis QRNG (7.8744 bits/byte), and significantly outperforms both Red Hat’s CPU Time Jitter RNG (7.4528 bits/byte) and Quside’s PCIe One quantum entropy source (6.5136 bits/byte). Even against specialized hardware RNGs like Microchip’s ECC608 (4.0568 bits/byte), QPP-RNG demonstrates superior performance using only general-purpose processors. By effectively transforming otherwise discarded system noise into a reliable and high-quality entropy stream, QPP-RNG establishes a novel paradigm for embedded security, providing a robust entropy source on general-purpose devices without specialized hardware. This makes it especially well-suited for resource-constrained Internet of Things (IoT) and edge computing applications where strong entropy sources are paramount.

Nonlinear dynamic systems and chaotic systems have been quite exhaustively researched in the domain of cryptography. However, the possibility of using fractional chaotic systems in the cryptosystem design has been much less explored while it bears advantages such as enlarged keyspace and better resistance to attack compared to classical nonlinear systems. This paper, therefore, proposes a novel structure for the pseudo-random number generator based on 3 different fractional chaotic systems, namely fractional Chen system, Lu system, and fractional generalized double-humped logistic map. Then, the outputs of this fractional chaotic pseudo-random number generator are used as a keystream for an image encryption scheme. The confusion layer of the scheme is conducted by a dynamic DNA encoding and decoding method combined with a 2D cat map for the permutation in the DNA bases level. The diffusion layer is performed through the adoption of a 32 bits discrete logistic map. The performance and security analyses have been conducted for the above-designed cryptosystem, proving that the proposed cryptosystem is practical and efficient, and can be successfully implemented in image encryption.