Cryptographic Key Generation Research Articles

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.

EEG-based identification and cryptographic key generation system using extracted features from transformer-based models

EEG-based identification and cryptographic key generation system using extracted features from transformer-based models

Integrated photonic cryptographic key generator using low-power MEMS-on-PIC technology

Integrated photonic cryptographic key generator using low-power MEMS-on-PIC technology

Security Analysis of the BB84 Protocol in IoT Networks

The Internet of Things (IoT) has experienced rapid growth, resulting in a proliferation of interconnected devices in domains such as smart homes, cities, and healthcare applications. Ensuring secure communication within IoT networks is crucial for protecting privacy and data. The BB84 protocol, a quantum key distribution (QKD) protocol, shows promise in enhancing IoT communication security. This paper presents a comprehensive security analysis of the BB84 protocol in IoT networks, examining key aspects including entropy, execution time, quantum bit error rate (QBER), and cryptographic key generation efficiency. The analysis aims to evaluate the protocol's resilience against potential attacks and its suitability for securing IoT communications. We delve into the evaluation of entropy levels in the key generation process to ensure strong cryptographic properties. We also examine the correlation between execution time and QBER to determine the protocol's efficiency and ability to counter timing-based attacks. Additionally, we analyze the efficiency of the keys generated by the BB84 protocol, taking into consideration factors such as key size, generation rate, and computational overhead. These evaluations allow us to assess the protocol's suitability for resource-constrained IoT devices

Leveraging generative adversarial networks for enhanced cryptographic key generation

Leveraging generative adversarial networks for enhanced cryptographic key generation

Tunable stochastic memristors for energy-efficient encryption and computing

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements – security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 (‘CuTeHO’) ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

Analysis the performance of Delay Locked Loop Physically Unclonable Functions for smart energy meter

The provision of secure and sustainable energy services is ensured by this research, also contributing to the advancement of technology align with the Sustainable Development Goals (SDGs). The motivation behind this study stems from the critical need to bolster hardware security within cutting-edge smart grid infrastructure, and more specifically, for smart energy metering technology. To address this need, this paper introduces a feasible and modular approach for enhancing the security through the implementation of a cryptographic key generator. This key generator is based on a modified Delay-based Physically Unclonable Function (PUF), which incorporates the innovative concept of a Delay Locked Loop(DLL).The reliability of the proposed PUFs has been rigorously assessed, demonstrating impressive performance levels of 98.02% and 99.1% across a wide temperature and supply voltage, spanning from -40°C to 80°C and (3.0-3.6) V. This is showcasing exceptional functionality within the smart meter’s operational parameters.The effectiveness of this approach is confirmed through practical testing conducted on the ZYNQ-7 ZC 702 Field-Programmable Gate Array (FPGA) platform.The outcomes are encouraging by substantial uniqueness (55.96% and 56.2%) and uniformity (51.2% and 49.15%). This research significantly advances the state of the art by surpassing previous investigations into XOR Arbiter PUF (XOR APUF) and Configurable Ring Oscillator PUF (CRO PUF) designs. Furthermore, the paper delves into an examination of the proposed design’s resilience against modeling attacks, along with comprehensive security assessments.

A 2D Cryptographic Hash Function Incorporating Homomorphic Encryption for Secure Digital Signatures.

User authentication is a critical aspect of any information exchange systemwhich verifies the identities of individuals seeking access to sensitive information. Conventionally, this approachrelies on establishing robust digital signature protocolswhich employ asymmetric encryption techniques involving a key pair consisting of a public key and its matching private key. In this article, a user verification platform constructed using integrated circuits (ICs) with atomically thin two-dimensional (2D) monolayer molybdenum disulfide (MoS2 ) memtransistors is presented. First, generation of secure cryptographic keys is demonstrated by exploiting the inherent stochasticity of carrier trapping and detrapping at the 2D/oxide interface trap sites. Subsequently, the ability to manipulate the functionality of logical NOR is leveraged to create a secure one-way hash function which when homomorphically operated upon with NAND, XOR, OR, NOT, and AND logic circuits generate distinct digital signatures. These signatures when subsequently decrypted, verify the authenticity of the receiver while ensuring complete preservation of data integrity and confidentiality as the underlying information is never revealed. Finally, the advantages of implementing a NOR-based hashing techniques in comparison to the conventional XOR-based encryption method are established. This demonstration highlights the potential of 2D-based ICs in developing critical hardware information security primitives.

Designing hardware for a robust high-speed cryptographic key generator based on multiple chaotic systems and its FPGA implementation for real-time video encryption

Recent advancements in communication technologies have highlighted the pivotal role of information security for all individuals and entities. In response, researchers are increasingly focusing on cryptographic solutions to ensure the reliability of confidential information. Recognizing the superiority of chaotic systems preference as entropy source of cryptographic systems, this paper proposes a novel true random number generator (TRNG) design by combining four different chaotic systems outputs, tailored for real-time video encryption application. These chaotic systems are continuous-time Lorenz and fractional-order Chen-Lee systems, as well as discrete-time Logistic and Tent maps. This study generates true random bit (TRB) sequences at a high bit rate (25 Mbps) through the hardware implementations of four distinct chaotic systems to have the best statistical randomness in the resulting output. Then, the cryptographic true random key bits (8-bit at 25 MHz frequency) are employed in the post-processing with real-time video data by using the XOR operation, a fundamental post-processing algorithm. The real-time video encryption application is executed on an experimental assembly, composed of a Field Programmable Gate Array (FPGA) development kit, an OV7670 camera module, a VGA monitor, and prototype circuit boards for the chaotic systems. To evaluate the effectiveness of the proposed encryption system, several security assessments are conducted. These include NIST SP 800 − 22 statistical tests, FIPS 140-1 standards, chi-square tests, histogram and correlation analysis, and NPCR and UACI differential attack resilience tests. Consequently, the findings suggest that the presented real-time embedded cryptosystem is robust and suitable for secure communications, particularly in the realm of video transmission.

О влиянии вероятностных характеристик дискретных источников, формирующих криптографические ключи, на практическую секретности ключа

The mathematical model of a binary discrete source is proposed, which is close to the practical conditions for the operation of cryptographic key generation devices. The model takes into account the non-stationarity of such devices, as well as the presence of statistical dependencies between their output bits. Within the framework of the model, an achievable and easily computable lower estimate of the practical secrecy of the key is obtained. It is shown that with certain parameters of the model, the assessment allows us to draw meaningful conclusions about the cryptographic quality of keys, while other well-known estimates do not cope with this.

Heteronanostructured Field-Effect Transistors for Enhancing Entropy and Parameter Space in Electrical Unclonable Primitives.

Hardware security is not a new problem but is ever-growing in consumer and medical domains owing to hyperconnectivity. A physical unclonable function (PUF) offers a promising hardware security solution for cryptographic key generation, identification, and authentication. However, electrical PUFs using nanomaterials or two-dimensional (2D) transition metal dichalcogenides (TMDCs) often have limited entropy and parameter space sources, both of which increase the vulnerability to attacks and act as bottlenecks for practical applications. We report an electrical PUF with enhanced entropy as well as parameter space by incorporating 2D TMDC heteronanostructures into field-effect transistors (FETs). Lateral heteronanostructures of 2D molybdenum disulfide and tungsten disulfide serve as a potent entropy source. The variable feature of FETs is further leveraged to enhance the parameter space that provides multiple challenge-response pairs, which are essential for PUFs. This combination results in stably repeatable yet highly variable FET characteristics as alternative electrical PUFs. Comprehensive PUF performance analyses validate the bit uniformity, reproducibility, uniqueness, randomness, false rates, and encoding capacity. The 2D material heteronanostructure-driven electrical PUFs with strong FET-to-FET variability can potentially be augmented as an immediately deployable and scalable security solution for various hardware devices.

Deep learning-based biometric cryptographic key generation with post-quantum security

Deep learning-based biometric cryptographic key generation with post-quantum security

A robust architecture of ring oscillator PUF: Enhancing cryptographic security with configurability

Physically Unclonable Functions (PUFs) are becoming more widely recognized as tamper-resistant, high-entropy hardware security primitives. Leveraging the intrinsic manufacturing properties of integrated circuits, PUFs provide a lightweight, cost-effective technique for device identification and cryptographic key generation. Despite the existence of various PUF designs and architectures, the ring oscillator (RO) PUF stands out as one of the most notable. This significance is due to its simplicity of implementation and outstanding performance metrics. However, traditional RO-PUFs are often classified as weak PUFs because they support a limited size of input-output combinations. This paper introduces a configurable inversion unit designed to construct a lightweight, robust architecture configurable (RAC) RO-PUF. The proposed unit comprises an XOR gate, an XNOR gate, and a multiplexer. The RACRO-PUF significantly increases the size of generated output bits while efficiently utilizing minimal hardware resources. The performance of the RACRO-PUF stands out in evaluations, recording a uniqueness of 49.78 %, uniformity of 49.42 %, reliability of 97.72 %, and impressive randomness of 98.34 %.

Random Number Generators: Principles and 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.

Random bit generation based on a self-chaotic microlaser with enhanced chaotic bandwidth

Abstract Chaotic semiconductor lasers have been widely investigated for high-speed random bit generation, which is applied for the generation of cryptographic keys for classical and quantum cryptography systems. Here, we propose and demonstrate a self-chaotic microlaser with enhanced chaotic bandwidth for high-speed random bit generation. By designing tri-mode interaction in a deformed square microcavity laser, we realize a self-chaotic laser caused by two-mode internal interaction, and achieve an enhanced chaotic standard bandwidth due to the photon–photon resonance effect by introducing the third mode. Moreover, 500 Gb/s random bit generation is realized and the randomness is verified by the NIST SP 800-22 statistics test. Our demonstration promises the applications of microlasers in secure communication, chaos radar, and optical reservoir computing, and also provides a platform for the investigations of multimode nonlinear laser dynamics.

Salp Swarm Algorithm to solve Cryptographic Key Generation problem for Cloud computing

Cryptographic keys are long strings of random bits generated using specialized algorithms and help secure data by making it unpredictable to any adversary. Cryptographic keys are used in various cryptographic algorithms in many domains, i.e., Cloud computing, Internet-of-Things (IoT), Fog computing, and others. The key generation algorithms are essential in cryptographic data encryption and decryption algorithms. This work proposed a cryptographic key generation algorithm based on Shannon entropy and the Salp Swarm algorithm (SSA) for generating randomized keys. The proposed Cryptographic Key Generation algorithm utilizes the dynamic movement of salps to create high-quality, robust, and randomized keys against attacks. The transfer function and quantization method convert a salp into a cryptographic key. The proposed Cryptographic Key Generation algorithm has been evaluated on four transfer functions against three state-of-the-art swarm intelligence metaheuristics, i.e., particle swarm optimization, BAT, and grey wolf optimization algorithms. The keys of eight different bit lengths, i.e., 512, 256, 192, 128, 96, 80, 64, were generated and evaluated due to their applications in the different encryption algorithms, i.e., AES, DES, PRESENT, SIMON, SPECK, and 3DES. The simulation study confirms that the proposed key generation algorithm effectively produces secure cryptographic keys.

KeyEncoder: A secure and usable EEG-based cryptographic key generation mechanism

Nowadays, a rapid, easy, and convenient access to our private information is essential to carry out both personal and professional activities. In most cases, this information is sensitive and can be stolen due to its importance and the lack of security protocols. In this study we propose a time–invariant cryptographic key generation mechanism based on electroencephalogram (EEG) signals. We employed Discrete Wavelet Transform and autoencoders to extract the biometric features from the EEG signals. Using these features, we construct a scheme to generate secure seeds that can be used as inputs for secure hash functions and obtain cryptographic keys. The mechanism proposed preserves the privacy of the user, as the cryptographic key is generated for each new EEG signal received, avoiding the need of storing the key, previous EEG signals or any other information. Results show that the proposed mechanism is secure against random attacks, as a 0% of False Acceptance Rate is reported, while generating seeds with an entropy of 0.968 in less than 500 ms.

Deep learning-based models’ application to generating a cryptographic key from a face image

Generating cryptographic keys, such as passwords or pin codes, involves utilizing specialized algorithms that rely on complex mathematical transformations. These keys necessitate secure storage measures and complex distribution and processing mechanisms, which often incur substantial costs. However, an alternative approach emerges, proposing the generation of cryptographic keys based on the user's biometric data. Since one can generate keys "on the fly," there is no longer a requirement for key storage or distribution. These generated keys, derived from biometric information, can be effectively employed for biometric authentication, offering numerous advantages. Additionally, this alternative approach unlocks new possibilities for constructing information infrastructure. By utilizing biometric keys, the initiation of cryptographic algorithms like encryption and digital signatures becomes more streamlined and less burdensome in storing and processing procedures. This paper explores biometric key generation technologies, focusing on applying deep learning models. In particular, we employ convolutional neural networks to extract significant biometric features from human face images as the foundation for subsequent key generation processes. A comprehensive analysis involves extensive experimentation with various deep-learning models. We achieve remarkable results by optimizing the algorithm's parameters, with the False Reject Rate (FRR) and False Acceptance Rate (FAR) approximately equal and less than 10%. With code-based cryptographic extractors’ post-quantum level of security, we ensure the continued protection and integrity of sensitive information within the cryptographic framework.

Methods to Encrypt and Authenticate Digital Files in Distributed Networks and Zero-Trust Environments

The methods proposed in this paper are leveraging Challenge–Response–Pair (CRP) mechanisms that are directly using each digital file as a source of randomness. Two use cases are considered: the protection and verification of authenticity of the information distributed in storage nodes and the protection of the files kept in terminal devices operating in contested zero-trust environments comprised of weak signals in the presence of obfuscating electromagnetic noise. With the use of nonces, the message digests of hashed digital files can be unique and unclonable; they can act as Physical Unclonable Functions (PUF)s in challenge–response mechanisms. During enrollment, randomly selected “challenges” result in unique output data known as the “responses” which enable the generation and distribution of cryptographic keys. During verification cycles, the CRP mechanisms are repeated for proof of authenticity and deciphering. One of the main contributions of the paper is the development of mechanisms accommodating the injection of obfuscating noises to mitigate several vectors of attacks, disturbing the side channel analysis of the terminal devices. The method can distribute error-free cryptographic keys in noisy networks with light computing elements without relying on heavy Error Correcting Codes (ECC), fuzzy extractors, or data helpers.

Personal authentication and cryptographic key generation based on electroencephalographic signals

Brain signals have recently been proposed as a strong biometric due to their characteristics such as, uniqueness, permanence, universality, and confidentiality. There are many factors that affect the stability of EEG signals as a biometric for example, using different recording devices, variation in participant emotional states, performing different mental tasks and recording in temporally spaced sessions. Due to the non-stationary nature of EEG signals, there are still speculations about the stability of using them for generating a unique and repeatable cryptographic key. The challenge that faces all biometric based crypto-systems is to overcome the variation in biometric itself over time and to generate multiple unique keys from the same observation. In this work, we investigate the stability of using EEG signals as a biometric for both personal authentication and cryptographic key generation. The authentication process was tested using three datasets AMIGOS, DEAP, and SEED. Achieving accuracy of 96.23%,98.85%, and 99.89% respectively. The key generation process generates a set of different keys with different lengths from the same observation. Each key is unique and repeatable. The generated keys were examined using NIST test suite, scale index test, and autocorrelation test. Time complexity analysis of the key generation process was performed.