Cryptographic Applications Research Articles

Exploration and Development of Quantum Computing Algorithms for Optimization, Cryptography, and Machine Learning Applications

Quantum computing, with its potential to revolutionize computation, relies fundamentally on the development of efficient algorithms to leverage its unparalleled processing capabilities. This research delves into the creation, exploration, and refinement of quantum computing algorithms, focusing on their applications in optimization, cryptography, and machine learning. Quantum algorithms such as Shor's and Grover's have demonstrated remarkable advantages in factorization and search problems, while more recent innovations are tackling complex challenges in global optimization and data analysis. By integrating principles of quantum mechanics—such as superposition, entanglement, and interference—these algorithms promise exponential speed-ups over classical counterparts in specific domains. This study critically analyzes existing quantum algorithms, proposes advancements in hybrid quantum-classical frameworks, and explores their implications for practical problem-solving. Through simulations and theoretical evaluations, it aims to bridge the gap between quantum theory and real-world applications, contributing to the evolution of quantum computing as a transformative technology.

A novel Chua's based 2-D chaotic system and its performance analysis in cryptography.

In this study, the chaotic behavior of a second-order circuit comprising a nonlinear resistor and Chua's diode is investigated. This circuit, which includes a nonlinear capacitor and resistor among its components, is considered one of the simplest nonautonomous circuits. The research explores various oscillator characteristics, emphasizing their chaotic properties through bifurcations, Lyapunov exponents, periodicity, local Lyapunov region, and resonance. The system exhibits both stable equilibrium points and a chaotic attractor. Additionally, the second objective of this study is to develop a novel cryptographic technique by incorporating the designed circuit into the S-box method. The evaluation results suggest that this approach is suitable for secure cryptographic applications, providing insights into constructing a cryptosystem for images and text based on its complex behavior. Real-life data were analyzed using various statistical and performance criteria after applying the proposed methodology. These findings enhance the reliability of the cryptosystems. Moreover, The proposed methods are assessed using a range of statistical and performance metrics after testing the text and images. The cryptographic results are compared with existing techniques, reinforcing both the developed cryptosystem and the performance analysis of the chaotic circuit.

Enhancing the SRAM PUF with an XOR Gate

This study focuses on designing enhanced Physically Unclonable Functions (PUFs) based on SRAM devices and improving the security of cryptographic systems. Most SRAM PUFs are limited in their number of CRPs, which makes them vulnerable to enrollment attacks. In this research, we present an SRAM-based PUF design that greatly increases the number of CRPs and the entropy of the generated bits by performing exclusive-or (XOR) on the responses of two SRAM devices. This was implemented using a readily available development board, SRAM devices, and a user-friendly custom circuit board for cryptographic key generation. The cryptographic protocol was implemented using both C++ and python3. The proposed SRAM PUF design was experimentally demonstrated and showed substantial improvements in the security of various cryptographic applications as a hardware authentication device. It also addresses the specific vulnerabilities of legacy designs.

Laser-engraved holograms as entropy source for random number generators

Our study introduces a novel approach to true random number generation (TRNG) using speckle patterns generated by laser-engraved holograms on carbon fiber-reinforced polymer (CFRP) composite substrates. Unlike previous methods, our approach simplifies the process by generating the necessary image dataset from a single microscope image of the engraved hologram. We achieve a high extraction ratio of 76 %, demonstrating the effectiveness of our TRNG. Moreover, our method successfully passes rigorous statistical tests proposed by the National Institute of Standards and Technology (NIST), indicating its suitability for cryptographic and secure system applications. This work offers promising implications for enhancing security in various domains, from secure communication networks to IoT devices.

Cavity-enhanced induced coherence without induced emission

This paper presents a theoretical study of the enhancement of Zou-Wang-Mandel (ZWM) interferometry through cavity-enhanced spontaneous parametric down-conversion (SPDC) processes producing frequency-entangled biphotons. The ZWM interferometry shows the capability to generate interference effects between single signal photons via indistinguishability between the entangled idler photons. This paper extends the foundational principles of ZWM interferometry by integrating cavity-enhanced SPDCs, aiming to narrow photon bandwidths for improved coherence and photon pair generation efficiency, which is critical for applications in quantum information technologies, quantum encryption, and quantum imaging. This work explores the theoretical implication of employing singly resonant optical parametric oscillators within the ZWM interferometer to produce narrow-band single photons. By combining cavity-enhanced SPDCs with ZWM interferometry, this study fills a gap in current theoretical proposals, offering significant advancements in quantum cryptography and network applications that require reliable, narrow-band single photons.

Tunable Liquid Crystal Twisted‐Planar Fresnel Lens for Vortex Topology Determination

AbstractThe development of straightforward and effective methods to determine the topological charge of phase singular beams is crucial for broadening the applications of optical vortices. Here, a straightforward and elegant method for determining the topological charge of an optical vortex using an innovative switchable achromatic nematic liquid crystal Fresnel lens is proposed. The approach allows for the unambiguous determination of both the absolute value and the sign of the topological charge of a singular beam across a wide range of the visible spectrum, as demonstrated both experimentally and theoretically. The research outcomes are promising for applications in optical information transmission systems, cryptography, and quantum computing.

Mem‐Transistor‐Based Gaussian Error–Generating Hardware for Post‐Quantum Cryptography Applications

AbstractQuantum computing can potentially hack the information encrypted by traditional cryptographic systems, leading to the development of post‐quantum cryptography (PQC) to counteract this threat. The key principle behind PQC is the “learning with errors” problem, where intentional errors make encrypted information unpredictable. Intentional errors refer to Gaussian distributed data. However, implementing Gaussian distributed errors is challenging owing to computational and memory overhead. Therefore, this study proposes a Gaussian error sampler that employs the intrinsic Gaussian properties of nanometer‐scale semiconductor devices. The proposed Gaussian error sampler significantly reduces computational and memory overhead. This work comprehensively evaluates the effectiveness of the proposed device by conducting statistical normality tests and generating quantile–quantile plots. The optimal programming voltage is identified to be −5.25 V, and the experimental results confirmed the Gaussian distribution of error data generated by the proposed module, aligning closely with software‐generated Gaussian distributions and distinct from uniform random distributions.

Satellite dish-like nanocomposite as a breakthrough in single photon detection for highly developed optoelectronic applications

A nanocomposite with a unique satellite dish-like structure, termed arsenic (III) oxide iodide (AsO2I)/polypyrrole (Ppy) intercalated with iodide ions (AsO2I/Ppy-I), has been meticulously developed via a two-step process. It features natural satellite dish-like nanostructures, potentially serving as nanoresonators for efficient single photon absorption. With a bandgap of 2.8 eV, the AsO2I/Ppy-I nanocomposite efficiently absorbs photons in the UV and visible light regions, making it suitable for single photon detection. Impressive performance is seen in photocurrent sensitivity measurements, recording values of 0.017 mA.cm−2 under white light and 0.009 mA.cm−2 under monochromatic light at 340 nm. Additionally, it exhibits high responsivity and detectivity, with peak values at wavelengths of 340 nm and 440 nm associated with the diameter of the Satellite dish nanostructure. Cost-effectiveness and simple synthesis methods make it attractive for industrial applications, while its unique structural characteristics and enhanced optical properties position it as a valuable asset in optoelectronic technologies. It holds promise as a leading material in advanced quantum technology, marking a significant leap forward in optoelectronic technologies, with potential applications in quantum cryptography, communication, and computing.

Application of Cryptography and Steganography Techniques to Improving the Security of Text Messages with RC4 Algorithm and MSB Method

This study discusses the application of cryptography and steganography techniques to improve the security of text messages using the Rivest Code 4 (RC4) algorithm and the Most Significant Bit (MSB) method. In the ever-growing digital era, data security is a top priority due to the increasing threat of cybercrime that can harm many parties. RC4 is a cryptography algorithm known for its encryption and decryption speed, while the MSB method is an effective steganography technique for hiding information in digital images. This study aims to develop an application that is able to encrypt text messages with the RC4 algorithm and hide them in digital images using the MSB method. With this combination, data is not only encrypted but also hidden, thus providing two layers of security to protect information from unauthorized access. The results of the study show that the combination of RC4 cryptography and MSB steganography techniques successfully improves data security well. The developed application is able to protect sensitive information from the risk of data theft and cyber attacks. In addition, this technique is also easy to implement and can be applied in various sectors, such as banking, health, and business communications, to protect sensitive data from unauthorized access. Keywords: Cryptography, Steganography, Most Significant Bit (MSB)

Resilience evaluation of memristor based PUF against machine learning attacks

Physical unclonable functions (PUFs) have emerged as a favorable hardware security primitive, they exploit the process variations to provide unique signatures or secret keys amidst other critical cryptographic applications. CMOS-based PUFs are the most popular type, they generate unique bit strings using process variations in semiconductor fabrication. However, most existing CMOS PUFs are found to be vulnerable to modeling attacks based on machine learning (ML) algorithms. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology, introducing cryptographic and resilient random outputs. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device and the fabrication process level such as the cross-sectional area and variations. Our study focuses on building a machine learning analysis and attack framework of tools on Cu/HfO2-x/p++Si\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Cu/HfO_{2-x}/p^{++}Si$$\\end{document} memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using machine learning analysis tools, on-top of holistic NIST cryptographic randomness testing initially provided, to provide a high level of certainty in predicting the randomness output of the verified Memrister-based PUF. Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means++\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$++$$\\end{document}, Random Forest, XGBoost and LSTM, within efficient time, and data complexity. Our results yield low accuracy and ROC results of within 0.49-0.52\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.49-0.52$$\\end{document} and 0.49-0.52\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.49-0.52$$\\end{document} respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF.

Paving the Way for SQIsign: Toward Efficient Deployment on 32-bit Embedded Devices

The threat of quantum computing has spurred research into post-quantum cryptography. SQIsign, a candidate submitted to the standardization process of the National Institute of Standards and Technology, is emerging as a promising isogeny-based signature scheme. This work aimed to enhance SQIsign’s practical deployment by optimizing its low-level arithmetic operations. Through hierarchical decomposition and performance profiling, we identified the ideal-to-isogeny translation, primarily involving elliptic curve operations, as the main bottleneck. We developed efficient 32-bit finite field arithmetic for elliptic curves, such as basic operations, like addition with carry, subtraction with borrow, and conditional move. We then implemented arithmetic operations in the Montgomery domain, and extended these to quadratic field extensions. Our implementation offers improved compatibility with 32-bit architectures and enables more fine-grained SIMD acceleration. Performance evaluations demonstrated the practicality in low-level operations. Our work has potential in easing the development of SQIsign in practice, making SQIsign more efficient and practical for real-world post-quantum cryptographic applications.

Almost pairwise independence and resilience to deep learning attacks

Almost pairwise independence (API) is a quantitative property of a class of functions that is desirable in many cryptographic applications. This property is satisfied by Learning with errors (LWE)-mappings and by special Substitution-Permutation Networks (SPN). API block ciphers are known to be resilient to differential and linear cryptanalysis attacks. Recently, security of protocols against neural network-based attacks became a major trend in cryptographic studies. Therefore, it is relevant to study the hardness of learning a target function from an API class of functions by gradient-based methods. We propose a theoretical analysis based on the study of the variance of the gradient of a general machine learning objective with respect to a random choice of target function from a class. We prove an upper bound and verify that, indeed, such a variance is extremely small for API classes of functions. This implies the resilience of actual LWE-based primitives against deep learning attacks, and to some extent, the security of SPNs. The hardness of learning reveals itself in the form of the barren plateau phenomenon during the training process, or in other words, in a low information content of the gradient about the target function. Yet, we emphasize that our bounds hold for the case of a regular parameterization of a neural network and the gradient may become informative if a class is mildly pairwise independent and a parameterization is non-regular. We demonstrate our theory in experiments on the learnability of LWE mappings.

Exploring DNA Computers: Advances in Storage, Cryptography and Logic Circuits.

Over the last four decades, research on DNA as a functional material has primarily focused on its predictable conformation and programmable interaction. However, its low energy consumption, high responsiveness and sensitivity also make it ideal for designing specific signaling pathways, and enabling the development of molecular computers. This review mainly discusses recent advancements in the utilization of DNA nanotechnology for molecular computer, encompassing applications in storage, cryptography and logic circuits. It elucidates the challenges encountered in the application process and presents solutions exemplified by representative works. Lastly, it delineates the challenges and opportunities within this filed.

Accelerating Linear Congruential Generators with Carbon Nanotube Field-Effect Transistors

Abstract In the era of technological advancements, safeguarding information privacy has become paramount across sectors like defense, finance, healthcare, and more. At the heart of these security measures lies cryptography, a field that necessitates the efficient processing and secure storage of data. Our project focuses on the optimization of Linear Congruential Generators (LCG), pivotal for cryptographic applications and pseudorandom number generation. Here it has been pioneered the implementation of LCG using Carbon Nanotube Field-Effect Transistor (CNTFET) technology, targeting the reduction of leakage power through the innovative application of sleep techniques. This advanced approach not only embodies a leap towards minimizing energy consumption but also enhances the overall performance and reliability of cryptographic systems. By integrating sleep modes into the CNTFET-based LCG design, our methodology significantly curtails leakage power without compromising the generator’s efficiency or output quality. The successful implementation and synthesis of this design, employing cutting-edge CNTFET technology, underscore the potential for substantial improvements in power efficiency while maintaining high standards of security and randomness essential for cryptographic algorithms.

Scalable Photo-Responsive Physical Unclonable Functions via Particle Kinetics.

The increasing menace of counterfeiting and information theft underscores the urgent need for security platforms compatible with both micro- and nanoelectronics. Existing methods for anticounterfeiting labeling and cryptographic systems rely on unclonable patterns derived from the unpredictable variability of physical phenomena. However, these approaches impose limitations on the scalability of security components. Here we present a scalable platform for photoresponsive physically unclonable functions based on oxide particle kinetics in polymer solutions. The stochastic agglomeration process occurring during the formation of polymer films with dispersed oxide particles yields random patterns, with pixel sizes scalable from micro to nanoscales. We produce mechanically flexible and self-destructible optical unclonable function patterns utilizing oxide aggregates on a polymer film. Moreover, we establish a strategy for generating electrical unclonable patterns on a conducting polymer film. This involves covering the polymer film with an aggregate pattern mask, which serves as an ultraviolet-blocking layer for randomly exposing the film to ultraviolet ozone treatment. These unclonable patterns constitute robust and compact security systems, exhibiting effective resilience against machine-learning attacks (∼50% prediction error for training data sets of 1000). The developed scalable platforms for physically unclonable functions provide a hardware solution for robust cryptographic applications.

Degree of Crystal Structure Distortion-Induced Tunable LiGaO2 Long Persistent Luminescence for Optical Information Encryption.

Tunable long persistent luminescence (LPL) phosphor materials have great potential for optoelectronic cryptographic applications. However, the mainstream techniques of modulating LPL generally have the characteristics of complex preparation processes, demanding crystal field environments, or expensive dopant ions, which restrict large-scale commercial application. Herein, we develop a simple, high-efficiency, and low-cost strategy to optimize the LPL of LiGaO2(LGO):Cu2+ by changing the sintering time to regulate the degree of crystal structure distortion. The Cu2+ as charge compensation will substantially enhance the emission intensity of LGO by a factor of 11.02 originating from the appropriate ionic size and coordination mode. Besides, the LPL time of LGO:Cu2+ can be extended effectively to 2 h by adjusting the sintering temperature and time (900 °C@24 h). The extension mechanism is that Li and Ga can be substituted for each other more easily and induce crystal structure distortion due to the special crystal structure of LGO, resulting in an optimal trap concentration in LGO:Cu2+. Thus, our findings provide a simple way to modulate long persistent luminescence and further consider their potential impact on optical information encryption.

Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire

Abstract Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.

Linear Codes over $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$ and their Applications

In the paper, we explore the simplex and MacDonald codes over the finite ring $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$. Our investigation focuses on the unique properties of these codes, with the particular attention to their weight distributions and Gray images. The weight distribution is a crucial aspect as it provides insights into the error-detection and error-correction capabilities of the codes. Gray images play a significant role in understanding the structure and behavior of these codes. By examining the dual Gray images of simplex and MacDonald codes over $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$, we aim to develop efficient secret sharing schemes. These schemes benefit from the inherent properties of the codes, such as minimal weight and redundancy, which are essential for secure and reliable information sharing. Understanding the access structure of these schemes is vital, as it determines which subsets of participants can reconstruct the secret. Our study draws on various properties to elucidate this access structure, ensuring that the schemes are secure and efficient. Through this comprehensive analysis, we contribute to the field of coding theory by demonstrating how simplex and MacDonald codes over $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$ can be effectively utilized in cryptographic applications, particularly in designing robust and reliable secret sharing mechanisms.

FPGA based implementation of a perturbed Chen oscillator for secure embedded cryptosystems

This paper introduces an enhancement to the Chen chaotic system by incorporating a constant perturbation term d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d$$\\end{document} to one of the state variables, aiming to improve the performance of pseudo-random number generators (PRNGs). The perturbation significantly enhances the system's chaotic properties, resulting in superior randomness and increased security. An FPGA-based realization of a perturbed Chen oscillator (PCO)-derived PRNG is presented, tailored for embedded cryptosystems and implemented on a Nexys 4 FPGA card featuring the XILINX Artix-7 XC7A100T-1CSG324C integrated chip. The Xilinx-based system generator (XSG) tool is utilized to generate a digital version of the new oscillator, minimizing resource utilization. Experimental results demonstrate that the PCO-generated data successfully passes the NIST and TestU01 test suites. Additionally, statistical tests with key sensitivity are performed, validating the suitability of the designed PRNG for cryptographic applications. This establishes the PCO as a straightforward and efficient tool for multimedia security.

Singularity, Observability, and Independence: Unveiling Lorenz’s Cryptographic Potential

The key findings of this study include a detailed examination of the Lorenz system’s observability, revealing that it maintains high observability compared to other chaotic systems, thus supporting its potential use in cryptographic applications. We also investigated the singularity manifolds, identifying regions where observability might be compromised, but overall demonstrating that the system remains reliable across various states. Additionally, statistical tests confirm that the Lorenz system exhibits strong statistical independence in its outputs, further validating its suitability for encryption purposes. These findings collectively underscore the Lorenz system’s potential to enhance cryptographic security and contribute significantly to the field of secure communications. By providing a thorough analysis of its key properties, this study positions the Lorenz system as a promising candidate for advanced encryption technologies.