Encryption Process Research Articles

Audio encryption framework based on chaotic map and DNA encoding

In this work, an audio encryption framework is proposed based on the chaos theory and DNA encoding. Traditional encryption algorithms designed for text data might not be efficient for handling the bulk data of audio files, leading to slow processing times and increased storage requirements. Striking the right balance between robust encryption and efficient processing is crucial. Additionally, some encryption methods can introduce noise or artifacts into the audio signal, impacting its clarity. Maintaining high-fidelity audio quality while ensuring strong encryption remains an ongoing area of research. Despite these challenges, the need for secure audio communication is undeniable. The RCM chaotic map is used to introduce chaotic properties in the encryption framework. A novel pseudorandom bit generator approach is proposed and used for encryption purposes. The proposed work is a symmetric encryption approach and is completely lossless. The proposed approach uses some lightweight computational procedures that make the algorithm simple and computationally less expensive which makes it suitable for real-time applications. Furthermore, the proposed approach shows significant performance and resilience against various cryptographic attacks. A new scrambling algorithm is proposed to add a layer of security. The proposed scramble algorithm uses logistic map beside the RCM map. The proposed approach achieved 98.28 % NSCR value and 33.63 % UACI values on average. The experimental outcomes are encouraging for practical applications of the proposed approach.

Reversible Data Hiding in Encrypted Images Based on Preprocessing-Free Variable Threshold Secret Sharing

To solve the limitations of reversible data hiding in encrypted domains (RDH-ED) that cannot be applied to a distributed variable security environment, a novel RDH-ED scheme based on variable threshold image secret sharing (VTSIS) is proposed. Initially, a security vulnerability analysis of existing changing thresholds in a bivariate polynomial-based secret image sharing (TCSIS) method is conducted and validated through experiments. Subsequently, enhancements are made to the VTSIS scheme to rectify the identified security loopholes. During the image encryption process, additional data can be embedded into the redundancy of VTSIS, which results in a large embedding rate and high security. Finally, theoretical analysis and experimental proofs are carried out for the proposed scheme, and the results show that our scheme broadens the application scenarios of RDH-ED. Notably, the scheme eliminates the need for preprocessing and has the advantages of high security, a large embedding rate, and complete reversibility.

Hyperchaotic color image encryption scheme based on simultaneous color channel confusion-diffusion operations

Shortcomings have been identified in current color image encryption methods. Firstly, these methods encrypt each color channel separately, resulting in a time-consuming process and independent encrypted channels, which can make hacking easier. Secondly, the use of XOR operations between image pixel values and code values during encryption can be vulnerable. To address these issues, a novel algorithm is introduced that incorporates a new XOR operation and simultaneous encryption of color channels. This approach creates interdependence between the encrypted channels, reduces encryption time, and enhances security by introducing a more complex XOR operation. The proposed method employs a substitution technique that involves XOR operations between groups of pixels and codes, inspired by the principles of the fast Walsh-Hadamard transform algorithm. The encryption process involves several key phases that enhance the security and efficiency of the system. In the initial phase, line processing involves mixing lines from different channels and application of chaotic substitution permutation operations. Subsequently, a similar operation is applied to columns, and finally, the channels are divided into overlapping squared sub-blocks, with a newly XOR proposed chaos-based confusion operation simultaneously applied to the three-channel sub-blocks. These phases are designed to ensure interdependence between color channels and reduce encryption time, resulting in a more robust encryption method. With this method, the RGB cipher channels become mutually dependent, rendering decryption of one channel impossible without the others. The approach has been evaluated using appropriate metrics and found to be robust, efficient, and resistant to various attacks, outperforming recently published methods. It is suitable for modern image encryption applications, including those related to the Internet of Medical Things (IoMT).

A crosstalk-free multiple-image encryption scheme based on computational ghost imaging with binarized detection

Crosstalk noise is a main problem limiting the performance of multiple-image encryption (MIE) scheme. In this work, we proposed a crosstalk-free MIE scheme based on computational ghost imaging (CGI) with binarized detection. In the encryption process, the plaintext images are encrypted into intensity sequences by the CGI system and quantified into two levels to obtain binary ciphertext sequences, which does not cause severe degradation in decrypted image quality compared to traditional CGI. Then, for the binary ciphertext sequences, we can combine them into a decimal grayscale ciphertext. To enhance security, a pixel bit layer scrambling (PBLS) algorithm is designed to scramble the grayscale ciphertext to obtain the final ciphertext. In the decryption process, anyone of the plaintext images can be decrypted without being affected by other plaintext images after performing inverse PBLS algorithm on the ciphertext and extracting the binary ciphertext sequence. The effectiveness, robustness, encryption capacity and security of the proposed scheme are demonstrated by numerical simulations and theoretical analysis.

PENERAPAN ADVANCED ENCRYPTION STANDARD UNTUK SISTEM LOGIN DAN REGISTRASI PADA SISTEM INFORMASI PENJUALAN (STUDI KASUS:TOKO ILHAM BANJAR)

Data security in sales information systems has become a critical priority in today's digital era. Ilham Banjar Store needs a system that can effectively protect user data, especially in login and registration features, which are vulnerable to security threats. The lack of encryption mechanisms in the current system results in user data, such as passwords, being transmitted in plaintext, increasing the risk of unauthorized access and data modification. Therefore, this study aims to enhance the security of the Ilham Banjar Store's sales information system by implementing the Advanced Encryption Standard (AES) 256-bit Cipher Block Chaining (CBC) method in the login and registration processes. The research method used is Research and Development (RD), consisting of stages such as literature review, needs analysis, system design, implementation, testing, evaluation, and analysis. The implementation of the Advanced Encryption Standard (AES) involves the encryption processes of AddRoundKey, SubBytes, ShiftRows, and MixColumns, and the decryption processes are the inverse of these encryption steps. Testing includes measuring encryption and decryption times as well as verifying data integrity using HMAC (Hash-Based Message Authentication Code). The research results show that the performance testing indicates encryption and decryption times remain within acceptable limits, and the encrypted and decrypted data maintain their integrity. Thus, the new system meets both security and performance requirements.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals. In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images. Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, the zigzag generative adversarial network (ZZ-GAN) is used as the learning network to generate the secret key. The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operation in both encryption and decryption using the proposed ZZ-GAN. Encrypting the original image requires a secret key generated during encryption. After identification, the encrypted image is decrypted using the generated key to reverse the encryption process. Finally, speckle noise is removed from the encrypted image in order to reconstruct the original image. According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images.

Multiple-image authentication based on metasurface and phase retrieval with sparsity constraints

In this paper, a multiple-image authentication method is proposed based on metasurface and phase retrieval with sparsity constraints. In the encryption process, multiple plaintext images are firstly encrypted to a phase-only hologram by using phase retrieval algorithm with sparsity constraints and iterative Fourier transform algorithm, and then the phase-only hologram is converted into a metasurface hologram for multiple-image authentication with the help of a geometric-phase metasurface unit, greatly expanding the encoding information capacity of metasurface for authentication. In the authentication process, the reconstructed image of metasurface hologram is respectively authenticated with multiple plaintext images through nonlinear correlation authentication such that multiple identities of the user holding this metasurface can be simultaneously verified, overcoming the problem that the metasurface for authentication and user identity can only be one-to-one, and substantially improving the efficiency of identity authentication. In order to demonstrate the feasibility of the proposed multiple-image authentication method, a series of numerical simulations are performed, and the simulation results show that the proposed method exhibits high feasibility and security as well as authentication efficiency, and the metasurface for multiple-image authentication has strong robustness against cropping attack.

On-chip silicon photonic micro-ring processor lights up optical image encryption.

Optical image encryption has long been an important concept in the fields of photonic network processing and communication. Here, we propose a convolution-like operation-based optical image encryption algorithm exploiting a silicon photonic multiplexing architecture to achieve content security. Particularly, the encryption process is completed in a 3 × 3 cross-shaped photonic micro-ring resonator (MRR) array on chip. For the first time, to the best of our knowledge, this algorithm encodes information in an integrated intensity modulation, effectively reducing the encoding difficulty. Moreover, the high reliability and scalability of optical encryption are ensured using both linear and nonlinear operations on photonic chips according to characteristics of MRRs. As the encryption and decryption experiments show, the image restoration accuracy of our optical encryption algorithm exceeds 99% under real system noise at the pixel level, indicating its noise-robust property. Meanwhile, the peak signal-to-noise ratios of the restored and encrypted images are >60 and <15 dB, respectively, revealing both the high accuracy of the restored image and the small correlation between the encrypted and original images. This work adds to the rapidly expanding field of optical image encryption on photonic chips.

A Visually Meaningful Color-Video Encryption Scheme That Combines Frame Channel Fusion and a Chaotic System

Compared to text and images, video can show information more vividly and intuitively via a moving picture; therefore, video is widely used in all walks of life. However, videos uploaded or stored in various video applications have not been treated with any protection, and these videos contain a lot of sensitive information that is more likely to be compromised. To solve this problem, video encryption schemes have been proposed. However, the main concern with existing video encryption schemes is that the private information in the encrypted video should be effectively protected, and, thus, the pixel distribution of the original video can be greatly damaged in the process of encryption, resulting in no or poor visual usability of the encrypted video. To this end, a novel color-video encryption scheme is proposed, which can effectively protect video privacy information while retaining certain visual information, thus enhancing the usability of encrypted videos. Firstly, the R, G, and B channels of the original color video are viewed as a whole for splitting. The dimensions of the blocks are three-dimensional, and permutation encryption is performed in three-dimensional blocks, which eliminates the redundancy of information between the video frame channels. Secondly, after permutation encryption, the channels of the video frame are separated, and then each channel is divided into blocks. The shape of the blocks is a square, and substitution encryption and permutation encryption operations are performed in turn. The whole encryption process is combined with the 2D-LSM chaotic system to improve the security of the scheme, as well as to reduce the time. Extensive experiments have been carried out, and the results show that the proposed scheme allows the encrypted video to retain rough visual information and, at the same time, effectively protects privacy, achieving the goal of encrypted video security and usability.

Unmanned ship image encryption method based on a new four-wing three-dimensional chaotic system and compressed sensing

This paper presents a three-dimensional chaotic map with five equilibrium points, which, unlike the classic Lorenz system, possesses a symmetric four-wing structure. Through detailed dynamical analysis and verification, the excellent chaotic properties of this map have been proven. Based on this system, an improved lightweight unmanned surface vehicle (USV) image information encryption system is designed. Given the constraints of USV equipment, the system first compresses images to enhance their storage capacity and transmission efficiency. Subsequently, an enhanced lightweight Feistel network encryption method is developed, utilizing a diagonal interpolation permutation model to ensure the security of the encryption process and reduce its complexity through a block structure. Simulation experiments and data analysis demonstrate that this algorithm effectively ensures the security of image information within the USV system. The use of compressed sensing and a block encryption structure reduces the complexity of the encryption process, thereby enhancing both the encryption efficiency and the transmission efficiency.

CIPHER MAIL ANALYZER

Cryptomail Analyzer (CMA) is a critical solution that provides the ability to crack passwords and increase email security. This report details the critical role of CMAs in strengthening email security by decrypting, detecting, and identifying potential vulnerabilities in encrypted communications. Through extensive research, the report highlights the CMA's effectiveness in protecting confidentiality, ensuring integrity and mitigating the risks associated with email-based cyber threats. CMA not only detects weaknesses in the encryption process using standard encryption algorithms, but also provides an abstract solution. Additionally, CMA integrates seamlessly into existing email infrastructure, providing a non-intrusive yet powerful layer of protection against data leakage and unauthorized access. Additionally, CMA's intuitive interface and customizable features allow organizations to customize email security to specific needs and compliance requirements. Keywords: Cipher Mail Analyzer, email security, encryption analysis, confidentiality, integrity, vulnerability detection, cyber threats, encrypted communications, data protection, digital privacy, encryption protocols, remediation, infrastructure integration, customization, compliance, innovation, trust.

IoT data encryption and phrase search-based efficient processing using a Fully Homomorphic-based SE (FHSE) scheme

In this study, the Efficient Multikeyword Fully Homomorphic Search Encryption (EMK-FHSE) model is proposed to improve cloud storage security for sensitive data. When fully homomorphic encryption (FHE) and search encryption (SE) technologies are coupled, Fully Homomorphic Search Encryption (FHSE) is a strategy that realizes the shared information's controlled privacy and search security. As more and more encrypted data is kept on cloud servers (CSs), a single-keyword SE approach may cause multiple keyword index duplication concerns, making it challenging for CSs to search for the encrypted information. To reduce these problems, a novel efficiency bottleneck has been developed. An Adaptive Privacy-Preserving Fuzzy Multi-Keyword Search (APPFMK) approach is presented to address the difficulties of low search effectiveness in a single-keyword searching strategy and the high processing cost of the existing multi-keyword schemes. Cloud servers (CS) hold enormous volumes of encrypted data, and the necessary encrypted index is transmitted to the closest edge node (EN) to enable multi-keyword searches and supported decryption. According to security research, the EMK-FHSE multi-keyword index is safe in distinguishability under chosen keyword attacks. The results section compares the proposed model's search, storage, trapdoor, calculation, storage and validation times to those of several other models. The proposed model could achieve the following values: 60.81 kb for storage, 10.92 for the trapdoor, 6.85 ms for search, 0.44 ms for computation cost by changing the keyword in a trapdoor, 156.31 ms for computation cost by changing the keyword in a dictionary, 0.44 kb for storage cost by changing the keyword in a trapdoor, 1.81 kb for storage cost by changing the keyword in a dictionary and 0.016seconds for verification time, respectively.

Self-balancing Incremental Broad Learning System with privacy protection

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Large file encryption in a reduced-round permutation-based AES file management system

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.

Improved Vigenere approach incorporating pseudorandom affine functions for encrypting color images

This article presents an improvement to the traditional Vigenere encryption method, specifically adapted for the encryption of color images. This enhancement relies on the use of two chaotic maps widely employed in the field of cryptography. After vectorizing the original image and calculating the initialization value, which alters the seeding pixel to trigger the encryption process, our approach integrates two new large substitution tables. These tables are linked to confusion and diffusion functions, incorporating multiple reversible pseudo-random affine functions at the pixel level. Finally, a global permutation is applied to the entire resulting vector to increase the temporal complexity of potential attacks on our system. Simulations conducted on a diverse set of images of various sizes and formats demonstrate the resilience of our approach against any unexpected attacks.

A Prototype of Decentralized Applications (DApps) Population Management System Based on Blockchain and Smart Contract

The Indonesian population reached 270,20 million in 2020. Each resident is equipped with various secret identities. The COVID-19 pandemic has made all activities use technology as a basis, causing residents' identities to be stored digitally. Some applications that keep these identities experience data leaks. However, with the advent of Web3 and its emphasis on decentralization through blockchain, a new era of secure data management is possible. Blockchain, with its inherent security features, ensures that data stored is secure, difficult to damage or lose due to mutual consensus. Every transaction is recorded, making it easy to carry out the audit process. Therefore, this research will design and implement prototype dApps for secure population management, leveraging the superior security of blockchain technology. The initial stage of research is to conduct a literature study. Furthermore, it is to create designs such as system, infrastructure, and activity diagrams. Then do the development of the dApps prototype. The last is testing using OWASP ZAP and cost analysis. A dApps prototype was implemented on a blockchain. Every transaction is recorded and publicly viewable through the Etherscan platform. Other data stored on a blockchain have gone through an AES-256 encryption process with the data owner's account key so that the owner can only see the data. The results of the tests performed show that there is no high-level warning. The cost analysis results show that the most used costs are when deploying smart contracts and making new data. For further development, it is implementing permissionless blockchain and multi-accounts.

Confusion and Diffusion Techniques for Image Encryption Process Based on Chaos System

Face recognition uses biometric technologies to identify humans based on their facial characteristics. This method is commonly used to restrict information access control. The benefits of face recognition systems encompass their ease of use and security. The human face recognition process consists of face detection, face tracking, and face recognition. The process uses some algorithms: the Viola-Jones for face detection, the Kanade-Lucas-Tomasi (KLT) for face tracking, and the principal component analysis (PCA) for face recognition. Furthermore, this research proposed face recognition with an encryption process to protect the data stored in a database. The encryption process consists of two main processes: confusion and diffusion. The confusion process is randomizing the position of the original image pixels. This research utilized the Arnold’s cat map (ACM) for the confusion process, and the diffusion process was performed using the XOR operation with the key generated from the 1D chaos system. Three different 1D chaos systems, namely logistic map, Bernoulli map, and tent map, were compared to see which chaos system had the best encryption results. Tests were conducted by comparing various parameters on the three proposed 1D chaos systems, including correlation coefficient, histogram analysis, entropy value, number of pixel rate changes (NPCR), and unified average change intensity (UACI). Based on testing the image encryption results, the diffusion process utilizing the tent map produced the best image encryption compared to other chaotic systems.

Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption

Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

Improved Association Rule Mining-based Data Sanitisation with Blockchain for Secured Supply Chain Management

A supply chain management (SCM) method must include information sharing as a vital component in order to improve supply chain performance and boost an organisation’s strategic advantage. Since, due to a lack of trust concern over information leakage, and security breaches by nefarious individuals or groups, several organisations are hesitant to share information with their supply chain partners. This work presents a new supply chain management-based secure data transmission method. By using blockchain-based data storage, it is assumed that the manufacturers, suppliers, and customers would transfer data that must be kept private during transmission. As a consequence, this paper aims to provide an improved association rule mining with a data sanitisation scheme with an improved Apriori algorithm used in the proposed data sanitisation process. In particular, the Long Short-Term Memory (LSTM) will generate keys by considering the objective relying on the value of the preservation ratio, false rule generation, hiding failure, and degree of modification. The weights are adjusted via a novel Minkowski distance-based Namib beetle optimisation (MDNBO) technique, which also improves the performance of the LSTM model. The reverse process of encryption occurs when encrypted data are restored at the receiving end. By contrasting it with the old methods with regard to security as well, the proposed protected data in SCM with blockchain technology will be proved to be efficient.