Asymmetric Encryption Research Articles

Secured Data Storage on the Cloud Using Hybrid Cryptography

With the growing reliance on cloud computing for data storage, ensuring the security and confidentiality of sensitive data has become paramount. Despite its advantages, cloud storage is susceptible to various security threats such as data breaches, unauthorized access, and insider threats. Traditional cryptographic techniques like symmetric and asymmetric cryptography offer a measure of security but have limitations when used independently. This paper proposes a hybrid cryptographic approach that combines the strengths of both symmetric and asymmetric cryptography, ensuring a robust solution for secured data storage in the cloud. The hybrid approach leverages the speed and efficiency of symmetric algorithms and the strong security features of asymmetric algorithms, making them both secure and efficient. This research explores various hybrid encryption techniques, discusses their security features, and demonstrates how they enhance data protection on cloud platforms

StegaCrypt Integrating Hybrid Cryptography and Image Steganography

This paper discusses the combination of cryptography and steganography for digital data security. Cryptography and steganography are two of the most significant techniques in information security, to be used differently: data is secured against unauthorized use and ensured integrity by way of transforming it into some unreadable format, whilst steganography hides a file inside another file. The project aim is to synthesize the techniques for a robust system to protect secure communication and data. We use RSA cryptography for the encryption, which is the asymmetric algorithm of high-level security, and image-based steganography to place the encrypted data within a digital image. The processes of cryptography will ensure decrypting the data only in the hands of authorized recipient parties, and steganography provides additional protection on the data invisibility front. This project shows that this integration of two technologies will safeguard sensitive information from all types of threats. In view of various methods to combine cryptography and steganography, this project puts forward the significance of incorporating multi-layer security to combat different types of cyber-attacks. The implementation also includes an elaborated coding using algorithms and development of a GUI to facilitate user interaction with the system. This further ensures that it is not only technically sound but also user-friendly. The last system is to send confidential data toward the broader field of secure communications and information protection, efficiently, securely, and in a user-friendly way

Trustless privacy-preserving data aggregation on Ethereum with hypercube network topology

The privacy-preserving data aggregation is a critical problem for many applications where multiple parties need to collaborate with each other privately to arrive at certain results. Blockchain, as a database shared across the network, provides an underlying platform on which such aggregations can be carried out with a decentralized manner. Therefore, in this paper, we have proposed a scalable privacy-preserving data aggregation protocol for summation on the Ethereum blockchain by integrating several cryptographic primitives including commitment scheme, asymmetric encryption and zero-knowledge proof along with the hypercube network topology. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification. The analysis of the protocol is made with respect to two main perspectives as security and scalability including computational, communicational and storage overheads. In the paper, the zero-knowledge proof, smart contract and web user interface models for the protocol are provided. We have performed an experimental study in order to identify the required gas costs per individual and per system. The general formulation is provided to characterize the changes in gas costs for the increasing number of users. The zero-knowledge proof generation and verification times are also measured.

Comparative Study on Encryption Algorithms for Autism MRI Scan Images

Healthcare centre is the most susceptible sctor. Cyber-attack on medical institutions, such as hospitals and health care centre have increased year after year. Autism health care centre is one of the victims. Autism MRI scan images include sensitive and confidential information about a patient's health and other personal information. Therefore, image encryption is a fundamental aspect of information security, especially in today's digital era where the proliferation of images and the need to protect sensitive visual content are of paramount importance. In order to protect the confidentiality and integrity of images, image encryption techniques work to make sure that only authorised users can view and understand the visual data while prohibiting unauthorised access and modification. The pixel values and spatial properties of the image are hidden using a variety of cryptographic methods and key-based actions. Symmetric encryption algorithm, like Advanced Encryption Standard (AES), which use the same key for encryption and decryption operations, are among the famous image encryption techniques. After that, asymmetric encryption algorithm like Rivest-Shamir-Adleman (RSA), which employ two distinct keys for both encryption and decryption. Next, the research includes a hybridization method of Elliptic Curve Cryptography (ECC) with the Hill Cypher (HC) for image encryption and decryption. There are five different Autism MRI Scan images selected to be used as the input image for encryption process. Then, each image will have two different file format which is PNG and BMP and two different file size which is 256*256px and 512*512px. This research's goal is to compare how well symmetric encryption, asymmetric encryption and hybrid encryption work as three separate image encryption techniques. This can help users rapidly and efficiently speed up the encryption and decryption of images and enhance the security of MRI scan images. By applying differential analysis parameters like Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI) and statistical analysis parameters like Histogram Analysis (HA), the characteristics of the image encryption approach are examined. At the end of the experiment, AES encryption proves to be the best among the three types of encryption with better performance in terms of security with NPCR percentage of 99.72%, UACI percentage of 32.68% and has a higher speed of encryption which only takes 0.0238s but has the second-best distribution in HA of pixel value to frequency compares to ECC with HC.

Design and Performance of Privacy Protection Model for Big Data Transmission Based on Mixed Encryption

With the advent of the big data era, data security and privacy protection have become particularly important. Big data has advantages such as large scale and diverse types, but it also brings risks of personal privacy leakage and data abuse. However, the symmetric or asymmetric encryption techniques alone have limitations in big data security and privacy protection. Therefore, a privacy protection model for big data transmission based on mixed encryption is proposed and experimentally validated. The research results indicated that the asymmetric encryption algorithm used had an encryption time of less than 20 ms, and the key space occupation was only 0.031 Kb to 0.063 Kb. After improving the symmetric encryption algorithm, it achieved a lower correlation of 0.16 within 18 ms and increased the number of ciphertext transformations to an average of 82 bits. In the performance verification of mixed encryption technology for 60 MB data packets, the proposed mixed encryption technology took 273.1 ms, the decryption took 254.7 ms, the correlation was as low as 0.12, and the average resistance time in resisting violent attacks exceeded 100 s. The model proposed in the study improves the encryption and decryption speed while ensuring data security, which has important practical application value and theoretical significance for data privacy protection in big data environments.

A Comparative Analysis of AES, RSA, and 3DES Encryption Standards based on Speed and Performance

Information security is important in the digital era, with encryption emerging as a fixed solution to safeguard sensitive data from unauthorized access. This paper provides an overview of encryption by categorizing cryptographic algorithms into symmetric and asymmetric, along with hashing. Symmetric cryptography uses a single key for encryption and decryption, and asymmetric cryptography utilizes distinct public and private keys. Hashing maps input messages into compact bit strings. The literature survey explores the performance analysis of encryption algorithms, focusing on Rivest Shamir Adleman (RSA), Data Encryption Standard (DES), and Advanced Encryption Standard (AES), considering parameters like computation time and memory usage. Furthermore, the comparative analysis of encryption algorithms, including AES, RSA, and 3DES, is discussed. The paper delves into the motivation for cryptography in secure document transfer systems, emphasizing the need for confidentiality, integrity, and availability. Finally, the study presents detailed insights into AES, RSA, and 3DES encryption standards, highlighting their principles and applications.

An Efficient EHR Secure Exchange Among Healthcare Servers Using Light Weight Scheme

This work addresses the critical need for secure and patient-controlled Electronic Health Records (EHR) migration among healthcare hospitals' cloud servers (HHS). The relevant approaches often lack robust access control and leave data vulnerable during transfer. Our proposed scheme empowers patients to delegate EHR migration to a trusted Third-Party Hospital (TTPH); which is the Certification Authority (CA) while enforcing access control. The system leverages asymmetric encryption utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA), EEC and ECDSA added robust security and lightness EHR sharing. Patient and user privacy is managed due to anonymity through cryptographic hashing for data protection and utilizes mutual authentication for secure communication. Formal security analysis using the Scyther tool and informal analysis was conducted to validate the system's robustness. The proposed scheme achieved EHR integrity due to the verification of the communicated HHS and ensuring the integrity of the HHS digital certificate during EHR migration. Ultimately, the result achieved in the proposed work demonstrated the scheme's high balance between data security and accuracy of communication, where the best result obtained represented 7.7/ ms as computational cost and 1248 /bits as communication cost compared with the relevant approaches.

Encryption with Complex Variable and its Capabilities

Abstract. Cybersecurity is instrumental to the modern world. The most effective protection of data online is cryptography and encryption. There are two main types: symmetric and asymmetric. They employ important mathematical concepts to encode vital information. Nevertheless, the encryption field remains largely within the world of real numbers. This paper analyzes an encryption method presented by George Stergiopoulos et al. utilizing complex numbers and investigates its possible usage. The process involves investigating the necessary complex variable applications and a comparative scoring system which provides vital outlook on the promise of this new methodology. Subsequently, the investigation of the time complexity, security and encryption speeds provides a vital outlook on the practical uses in society. The results are promising feasibility of this new algorithm and the encouragement of further investigation into complex variable applications that encrypt with a more substantial range than any operation in the real numbers. Thus, the explicit novel insightful comparison of both symmetric and asymmetric encryption systems to the proposed complex encryption shows vital promise and further interest in investigation into this field as it opens the possibilities to an infinite array of novel complex operations that were previously inaccessible due to the restraint of real numbers.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Few-Shot Face Sketch-to-Photo Synthesis via Global-Local Asymmetric Image-to-Image Translation

Face sketch-to-photo synthesis is widely used in law enforcement and digital entertainment, which can be achieved by Image-to-Image (I2I) translation. Traditional I2I translation algorithms usually regard the bidirectional translation of two image domains as two symmetric processes, so the two translation networks adopt the same structure. However, due to the scarcity of face sketches and the abundance of face photos, the sketch-to-photo and photo-to-sketch processes are asymmetric. Considering this issue, we propose a few-shot face sketch-to-photo synthesis model based on asymmetric I2I translation, where the sketch-to-photo process uses a feature-embedded generating network, while the photo-to-sketch process uses a style transfer network. On this basis, a three-stage asymmetric training strategy with style transfer as the trigger is proposed to optimize the proposed model by utilizing the advantage that the style transfer network only needs few-shot face sketches for training. Additionally, we discover that stylistic differences between the global and local sketch faces lead to inconsistencies between the global and local sketch-to-photo processes. Thus, a dual branch of the global face and local face is adopted in the sketch-to-photo synthesis model to learn the specific transformation processes for global structure and local details. Finally, the high-quality synthetic face photo can be generated through the global-local face fusion sub-network. Extensive experimental results demonstrate that the proposed Global-Local Asymmetric (GLAS) I2I translation algorithm compared to SOTA methods, at least improves FSIM by 0.0126, and reduces LPIPS (alex), LPIPS (squeeze), and LPIPS (vgg) by 0.0610, 0.0883, and 0.0719, respectively.

Adaptive Deep Ant Colony Optimization–Asymmetric Strategy Network Twin Delayed Deep Deterministic Policy Gradient Algorithm: Path Planning for Mobile Robots in Dynamic Environments

Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynamic environments. Addressing these challenges, this study introduces an adaptive deep ant colony optimization (ADACO) algorithm, which significantly improves efficiency and convergence speed through enhanced pheromone diffusion mechanisms and updating strategies, applied to global path planning. To adapt to dynamically changing environments and achieve more precise local path planning, an asymmetric strategy network TD3 algorithm (ATD3) is further proposed, which utilizes global path planning information within the strategy network only, creating a new hierarchical path planning algorithm—ADACO-ATD3. Simulation experiments demonstrate that the proposed algorithm significantly outperforms in terms of path length and number of iterations, effectively enhancing the mobile robot’s path planning performance in complex dynamic environments.

An Enhanced Learning with Error-Based Cryptosystem: A Lightweight Quantum-Secure Cryptography Method

Quantum-secure cryptography is a dynamic field due to its crucial role in various domains. This field aligns with the ongoing efforts in data security. Post-quantum encryption (PQE) aims to counter the threats posed by future quantum computers, highlighting the need for further improvement. Based on the learning with error (LWE) system, this paper introduces a novel asymmetric encryption technique that encrypts entire messages of n bits rather than just 1 bit. This technique offers several advantages including an additive homomorphic cryptosystem. The robustness of the proposed lightweight public key encryption method, which is based on a new version of LWE, ensures that private keys remain secure and that original data cannot be recovered by an attacker from the ciphertext. By improving encryption and decryption execution time—which achieve speeds of 0.0427 ms and 0.0320 ms, respectively—and decreasing ciphertext size to 708 bits for 128-bit security, the obtained results are very promising.

Applying Polynomials for Developing Post Quantum Cryptography Algorithms to Secure Online Information - An Initial Hypothesis

In the contemporary era, a vast array of applications employs encryption techniques to ensure the safeguarding and privacy of data. Quantum computers are expected to threaten conventional security methods and two existing approaches, namely Shor's and Grover's algorithms, are expediting the process of breaking both asymmetric and symmetric key classical algorithms. The objective of this article is to explore the possibilities of creating a new polynomial based encryption algorithm that can be both classically and quantum safe. Polynomial reconstruction problem is considered as a nondeterministic polynomial time hard problem (NP hard), and the degree of the polynomials provide the usage of scalable key lengths. The primary contribution of this study is the proposal of a novel encryption and decryption technique that employs polynomials and various polynomial interpolations, specifically designed for optimal performance in the context of a block cipher. This study also explores various root convergence techniques and provides algorithmic insights, working principles and the implementation of these techniques, which can potentially be utilized in the design of a proposed block-cipher symmetric cryptography algorithm. From the implementation, comparison and analysis of Durand Kernal, Laguerre and Aberth Ehrlich methods, it is evident that Laguerre method is performing better than other root finding approaches. The present study introduces a novel approach in the field of polynomial-based cryptography algorithms within the floating-point domain, thereby offering a promising solution for enhancing the security of future communication systems.

Asymmetric Spatiotemporal Online Learning for Deep Spiking Neural Networks

Although the precise symmetric forward and feedback connections between neurons are thought to be impossible in the brain, most existing deep spiking neural networks (SNNs) spatio-temporal learning algorithms still require such a strong architectural constraint to complete tasks. Besides that, as an expected feature for specialized neuromorphic hardware to be deployed, effective online learning of deep SNNs for training spatio-temporal data are still lacking. To address these issues, an effective biologically plausible asymmetric spatio-temporal online learning algorithm called ASTOL is proposed for training deep SNNs in real-time. ASTOL could learn the spatial and temporal features simultaneously in real-time, without the precise weights symmetric backpropagation and the need to know the duration of spatio-temporal data in advance. Experimental results show that the proposed ASTOL algorithm achieves comparable performance in real-time learning on the rate coding MNIST dataset and the temporal coding music MedleyDB dataset and speech TIDIGITS dataset with other state-of-the-art algorithms with simpler learning mechanism way as it avoids transport of synaptic weights. Besides that, the experiment on MNIST datasets also shows that ASTOL can use simpler network structures to achieve good performance faster.

Precisely regulation of photochromism via absorption-induced photoreaction attenuation and light intensity dependence for time-resolved asymmetric encryption

With the growing need for secure information storage, encryption materials are required to be designed with high information capacity, easy to prepare, and hard to decode. Here, donor–acceptor Stenhouse adducts (DASAs), known for their straightforward synthesis, are used to develop a new time-resolved asymmetric encryption strategy, enhancing information capacity. The photoswitching property of DASAs is regulated by the intensity of initial absorbance and the intensity of light irritation. Besides, we recognize the relationship between the intensity of initial absorbance and photoswitching rate constant is caused by the absorption-induced photoreaction attenuation (AIPA). By rationally regulating the photo-bleching time of DASAs, the information can be sequentially revealed (colored state) or concealed (bleached state) in the desired temporal order. As a result, a quick response (QR) code label for practical application and a multi-level encoding scheme for sophisticated asymmetric encryption have been proposed. Specifically, the advanced asymmetric encryption technique employs layer sequence and light intensity as private keys, diverging from traditional public key systems. Information is sequentially displayed over time. The accurate information can only be revealed at the correct temporal dimension (layer sequence) and the appropriate decoding parameter (light intensity), both of which are precisely controlled by AIPA. This study not only unveils an approach to time-resolved asymmetric encryption using DASAs, enhancing the security and capacity of information storage, but also paves the way for the development of high-security encryption materials that are both highly efficient and resistant to unauthorized decoding efforts.

Blockchain-machine learning fusion for enhanced malicious node detection in wireless sensor networks

In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure real-time responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorr-like zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.

Performance Analysis of symmetric and asymmetric Encryption Algorithms Based on File, Image and Video

The rapid evolution of digital technology has exponentially amplified the generation and sharing of diverse digital data, notably files, images, and videos, over the internet, intensifying the critical need for enhanced security measures to safeguard these prevalent data types. This research presents a comprehensive analysis of various encryption algorithms applied to different data types - files, images, and videos. The study categorizes encryption algorithms into symmetric and asymmetric types, with examples including AES, DES, Triple DES, RSA, Diffie-Hellman, and ECC. The paper further explores specific algorithms used for file, image, and video encryption. The objective is to identify the most efficient encryption algorithm for each data type, thereby enhancing data security in the digital age. The paper emphasizes that while encryption is a crucial tool for data security, it should be used in conjunction with other security measures for comprehensive protection.

Blockchain-based security-minded information-sharing in precast construction supply chain management with scalability, efficiency and privacy improvements

Blockchain and Interplanetary File System (IPFS) integration holds great promise for enhancing transparency and traceability in precast construction supply chain management (PCSCM). However, such integration faces challenges regarding unauthorized access to confidential data, which can lead to significant consequences, such as financial losses and legal issues. Regarding this gap, this paper proposes a hybrid privacy-preserving access control mechanism for securing blockchain-IPFS information-sharing in PCSCM with confidentiality considerations. Multiple privacy-preserving techniques (e.g., information-sharing channels, symmetric and asymmetric encryption, and lightweight proxy re-encryption) are leveraged. A prototype system demonstrates its feasibility and effectiveness, achieving demonstrably low network latency (millisecond level), efficient encryption (millisecond level), and robust data security within both blockchain and IPFS. This research contributes to a deeper understanding of blockchain-IPFS integration and provides a valuable reference point for future research and practical adoption.

Asymmetry index for data and its verification in dimensionality reduction and data visualization

We propose an asymmetry index as a measure of degree of asymmetry of a given dataset. It provides an additional information on a dataset allowing to guide and improve any further analysis. The index reflects the intensity of the asymmetric relationships among data resulting from hierarchical data structure. Using the information retrieved by our asymmetry index, one obtains a justification and explanation of the effectiveness of the subsequent asymmetric data analysis methods, as well as helpful preparation to asymmetrizing the tools for the further analysis. The asymmetry index is based on the k-nearest neighbors graph representing the considered data. Therefore, it uses the intrinsic geometry-based information on the data, in this way, providing an insight into the data structure. Our experiments on real data are designed to verify the usefulness of the asymmetry index and the correctness of its theoretical fundamentals. In our empirical validation, we employ the symmetric and asymmetric dimensionality reduction algorithms and evaluate their results on the basis of clustering in the 2-dimensional visualization space. We test, whether our index indeed predicts the level of superiority of the asymmetric methods over their symmetric counterparts.

An Efficient Image Encryption Scheme for Medical Image Security

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security. The efficacy of the proposed encryption scheme is validated through comprehensive experimental evaluations, which demonstrate its proficiency in ensuring data security while maintaining computational efficiency. The scheme's compatibility with existing medical imaging systems is also examined, affirming its seamless integration into contemporary healthcare infrastructures. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. The primary contributions include the development of a DNA-based encryption algorithm and a novel key management system, both of which significantly enhance the security of medical images. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. By safeguarding the confidentiality and integrity of medical images, the proposed scheme empowers healthcare providers to uphold patient privacy and trust in the digital age. Experimental results show that this approach ensures robust encryption without compromising image quality, making it suitable for sensitive medical imaging applications.