Matrix Encoding Research Articles

Two-dimensional hyperchaos-based encryption and compression algorithm for agricultural UAV-captured planar images.

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability.

Solid state NMR spectral editing of histidine, arginine and lysine using Hadamard encoding.

The NMR signals from protein sidechains are rich in information about intra- and inter-molecular interactions, but their detection can be complicated due to spectral overlap as well as conformational and hydrogen exchange. In this work, we demonstrate a protocol for multi-dimensional solid-state NMR spectral editing of signals from basic sidechains based on Hadamard matrix encoding. The Hadamard method acquires multi-dimensional experiments in such a way that both the backbone and under-sampled sidechain signals can be decoded for unambiguous editing in the 15 N spectral frequency dimension. All multi-dimensional 15 N-edited solid-state NMR experiments can be acquired using this strategy, thereby accelerating the acquisition of spectra spanning broad frequency bandwidth. Application of these methods to the ferritin nanocage, reveals signals from N atoms from His, Arg, Lys and Trp sidechains, as well as their tightly bound, ordered water molecules. The Hadamard approach adds to the arsenal of spectroscopic approaches for protein NMR signal detection.

Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm

Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.

MOREM: An evolutionary multitasking optimization algorithm for multi-objective recommendations

Multi-objective evolutionary algorithms (MOEAs) have been successfully applied in recommender systems, where the recommendation is modeled as a multi-objective optimization problem by considering both accuracy and non-accuracy metrics. However, to recommend to all users, the population individuals in most of the exiting MOEA-based recommendation algorithms are usually encoded in the form of matrix encoding, and with the increased number of users and items, it will lead to a large search space. To this end, in this paper, we propose a novel evolutionary multitasking algorithm MOREM to tackle the challenge of multi-objective recommendations, where a related auxiliary task is constructed by both user and item reduction to help solving the complex original task by knowledge transfer. Specifically, a task generation strategy for creating auxiliary task is firstly proposed to cluster users by user-rating information (i.e., user reduction), where only a part of important items (i.e., item reduction) are recommended for the central user within each cluster with the aim to greatly reduce the size of the matrix encoding. In addition, a novel knowledge transfer mechanism is proposed, which can effectively achieve knowledge migration between the two tasks. Experimental results on real-world datasets show that MOREM outperforms several state-of-the-art algorithms for multi-objective recommendations.

A Secure Image Cryptographic Algorithm Based on Triple Incorporated Ciphering Stages

Lately, image encryption has stand out as a highly urgent demand to provide high security for digital images against use and unauthorized distribution. A lot of existing researches use chaotic systems, symmetric or asymmetric schemes for image encryption, but cryptosystem based on one encryption technique only, faces many challenges like weak security and low complexity. Therefore, incorporating two or more different ciphering methods yields a secure and efficient algorithm to protect image information. In this work, a new image cryptosystem is suggested by joining zigzag scan technique, RSA algorithm and chaotic systems. These three security factors introduce Triple Incorporated Ciphering stages system (TIC). Initially, the plaintext image is divided into $8\times 8$ non-overlapping blocks, then the odd blocks are isolated from the even blocks. After that, a new modified zigzag scan in two different directions is adopted for shuffling pixels in the odd and even blocks. This operation effectively enhances the shuffling degree. Next, the RSA algorithm is utilized after combining the scrambled blocks in one matrix. Finally, chaotic systems are implemented on the resultant encrypted matrix to complete the ciphering process. The chaos is implemented in two steps; confusion and diffusion. Duffing map is exploited in the confusion stage, whereas L"u system is adopted on the shuffled matrix in the diffusion stage. The simulation results show the superiority of TIC in both security and attacks robustness compared to other cryptographic algorithms. Therefore, TIC can be exploited in real-time communication systems for secure image transmission.

Efficient and Accurate Cloud-Assisted Medical Pre-Diagnosis With Privacy Preservation

The emergence of cloud computing enables various healthcare institutions to outsource pre-diagnostic models and provide timely and convenient services for patients. However, healthcare institutions and patients have serious concerns about potential privacy leakage as cloud servers cannot be fully trusted. In this paper, a privacy-preserving cloud-assisted medical pre-diagnosis scheme, named NAIAD, is proposed, where patients can securely query the outsourced model and obtain their pre-diagnostic results. Specifically, the pre-diagnostic model is constructed on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN), and Mahalanobis Distance (MD) is chosen as the similarity metric to achieve high accuracy. Accordingly, a secure MD-based comparison method (SMDC) is designed based on a matrix encryption technique. The method is a basic module of NAIAD that enables cloud servers to compare encrypted medical records and achieve privacy-preserving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN-based pre-diagnosis with linear complexity. To further improve the computational efficiency, medical records are first clustered and encrypted to construct a hierarchical index tree, then patients can query the tree to speed up the query process. Detailed security analysis indicates NAIAD can resist closeness-same-pattern chosen-plaintext attack, and extensive experiments on real-world and synthetic databases demonstrate NAIAD has high query efficiency and pre-diagnosis accuracy.

A 19-Bit Small Absolute Matrix Encoder.

With the application of encoders in artificial intelligence and aerospace, the demand for the miniaturization and high measurement accuracy of encoders is increasing. To solve this problem, a new absolute matrix encoder is proposed in this paper, which can realize 19-bit matrix coding by engraving two circles of matrix code, and has the advantages of fewer circles of code disk engraving and higher measurement accuracy. This article mainly focuses on the design of a new matrix code disk, encoding and decoding methods, decoding circuit design, Matlab simulation analysis, and experimental error analysis. The experimental results show that the encoder designed in this paper achieves ultra-small volume Φ30 mm × 20 mm, and the angle measurement accuracy is 2.57".

Verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing

Abstract A verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing is proposed. Firstly, the plain image is compressed and encrypted by 2D block compressive sensing to obtain the pre-encrypted image. During this process, the enhanced logistic map and the enhanced tent map are used to generate the measurement matrix and the permutation sequence, respectively. Secondly, multiple shadow images of the pre-encrypted image are generated by using the (t, n)-threshold secret sharing scheme, and then each shadow image is further encrypted by using the encryption matrix generated by the chaotic system. Meanwhile, the signatures of the shadow images are obtained by using RSA signature algorithm. Finally, the shadow images and their corresponding signatures are embedded into the carrier image to generate the cipher image with high visual quality by using the LSB method. Additionally, the present algorithm can resist known-plaintext and chosen-plaintext attacks by incorporating the hash value of the plain image as part of the key. Meanwhile, the use of 2D block compressive sensing significantly reduces the reconstruction time. Simulation results demonstrate that the proposed algorithm achieves excellent decryption quality and operational efficiency.

Bagua Protocol: A Whole-Process Configurable Protocol for IoT Sensing Devices Security Based on Strong PUF

The Internet of Things (IoT) plays an important role in all aspects of production and day-to-day life. However, owing to the frequently trusted authentication vulnerabilities, the physical unclonable function (PUF) has unique advantages in the field of equipment authentication because of its non-storage and non-volatility. Nevertheless, PUFs are vulnerable to machine learning (ML) attacks. Once a model is constructed accurately, the secrecy of the PUF is lost. Therefore, Bagua matrices are proposed in this study, which can greatly reduce the accuracy of modeling by encrypting the challenge information. On this basis, a whole-process configurable IoT sensing device protocol was constructed for authentication and transmission, and different matrix encryption methods were configured according to the needs of the different devices. Moreover, on the premise of trusted authentication, the perceptual information can be encrypted using a preset matrix. According to the implementation results of the scheme, the resistance to the ML attacks of the PUF improved significantly and the device authentication and encrypted transmission could operate normally.

Efficient Privacy-preserving Logistic Model With Malicious Security.

Conducting secure computations to protect against malicious adversaries is an emerging field of research. Current models designed for malicious security typically necessitate the involvement of two or more servers in an honest-majority setting. Among privacy-preserving data mining techniques, significant attention has been focused on the classification problem. Logistic regression emerges as a well-established classification model, renowned for its impressive performance. We introduce a novel matrix encryption method to build a maliciously secure logistic model. Our scheme involves only a single semi-honest server and is resilient to malicious data providers that may deviate arbitrarily from the scheme. The -transformation ensures that our scheme achieves indistinguishability (i.e., no adversary can determine, in polynomial time, which of the plaintexts corresponds to a given ciphertext in a chosen-plaintext attack). Malicious activities of data providers can be detected in the verification stage. A lossy compression method is implemented to minimize communication costs while preserving negligible degradation in accuracy. Experiments illustrate that our scheme is highly efficient to analyze large-scale datasets and achieves accuracy similar to non-private models. The proposed scheme outperforms other maliciously secure frameworks in terms of computation and communication costs.

A Community Division-Based Evolutionary Algorithm for Large-Scale Multi-Objective Recommendations

Multi-objective evolutionary algorithms (MOEAs) have been demonstrated to be competitive in recommender systems. In most of the exiting MOEA-based recommendation algorithms, individuals in the population are usually represented in the form of matrix encoding, where rows represent users and columns represent recommended items. However, as the number of users and items increases, the search space of these MOEA-based methods increases exponentially. To tackle the issue, in this paper, we suggest a community division-based evolutionary algorithm named ComEA for large-scale multi-objective recommendations, where a community division-based reduction scheme is proposed to greatly reduce the search space of MOEA from both row and column perspective. To be specific, a user network is firstly constructed by using the user-rating information. Based on the constructed network, we propose a community division-based row reduction strategy to reduce the dimensionality of rows, and then propose a community division-based column reduction strategy to reduce the dimensionality of columns. With the proposed community division-based reduction strategies, the search space of the proposed ComEA can be greatly reduced. Experimental results on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">movielens, food</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">douban</i> datasets demonstrate the superiority of the proposed algorithm over several state-of-the-art multi-objective algorithms for large-scale recommendation problem.

Matrix Encryption Walks for Lightweight Cryptography

In this paper, we propose a new symmetric stream cipher encryption algorithm based on Graph Walks and 2-dimensional matrices, called Matrix Encryption Walks (MEW). We offer example Key Matrices and show the efficiency of the proposed method, which operates in linear complexity with an extremely large key space and low-resource requirements. We also provide the Proof of Concept code for the encryption algorithm and a detailed analysis of the security of our proposed MEW. The MEW algorithm is designed for low-resource environments such as IoT or smart devices and is therefore intended to be simple in operation. The encryption, decryption, and key generation time, along with the bytes required to store the key, are all discussed, and similar proposed algorithms are examined and compared. We further discuss the avalanche effect, key space, frequency analysis, Shannon entropy, and chosen/known plaintext-ciphertext attacks, and how MEW remains robust against these attacks. We have also discussed the potential for future research into algorithms such as MEW, which make use of alternative structures and graphic methods for improving encryption models.

ROMSS: a rational optional multi-secret sharing scheme based on reputation mechanism

The traditional threshold secret sharing scheme only allows the participants’ sub-secret shares to be used once in the reconstruction process. Several multi-secret sharing schemes have been proposed that are related to cloud computing, aiming to improve reconstruction efficiency. Rational secret sharing is a technique that combines secret sharing with game theory. In traditional rational multi-secret sharing, participants must reconstruct all secrets, resulting in unnecessary overhead. Rational participants will act dishonestly to maximize their own interests, leading to a prisoner’s dilemma and incomplete secret reconstruction. Additionally, when sharing multiple secrets, the Dealer must distribute the sub-secret shares of all secrets to the participants, increasing overhead. In this paper, we propose a rational optional multi-secret sharing scheme based on a reputation mechanism that selectively reconstructs secrets according to participants’ needs in the context of cloud computing. Our scheme introduces a reputation mechanism to evaluate participants’ reputation values to avoid their dishonest behaviors. Furthermore, we adopt a broadcast encryption matrix so that participants only need to receive a single sub-secret share to participate in multi-secret reconstruction. Our security analysis shows that the proposed scheme can effectively constrain the self-interested behavior of rational participants and reduce the overhead in the process, thus multi-secret sharing scheme can provide more efficient and secure solutions for secret sharing in key management and distributive storage for the cloud scenarios.

A Facile Approach To Represent Neural Network With Less Storage Space

Deep neural networks have been applied to different applications, such as image recognition, data analysis, natural language processing, and so on. Some companies regularly release their mobile applications to app stores. Usually, app stores have a restriction that overweight applications can not be downloaded until the smartphone connects to Wi-Fi. Unfortunately, the size of the application that consists of deep neural networks may be too large. In order to reduce the size of application, an efficient compression approach is highly desired. An efficient encoding for compressed neural networks is a critical point for reducing storage complexity. Most of previous studies focus on the sparse matrix encoding instead of the dense one. The dense matrix encoding methods have not been studied well. Meanwhile, sparse matrix encoding can not attain a high compression rate when encoding a dense matrix. Toward this end, we proposed an alternative compression algorithm that linearizes the weights on each layer and then stores linearized weights with critical information. For a dense matrix, our algorithm can achieve a higher compression rate than one existing sparse matrix encoding. Experiment results indicate that our proposed piecewise linearization scheme can make VGG-16, Resnet152, and Densenet169 achieve at least two times of the compression rates. In other words, neural networks parameters can be stored in 40–50% of the original space.

Explore the potential of deep learning and hyperchaotic map in the meaningful visual image encryption scheme

AbstractIn recent years, meaningful visual image encryption schemes that the plain image is compressed and encrypted and then hidden into the carrier image have received increasing attention. This paper proposes a new meaningful visual image encryption scheme, which consists of three stages: compression (compression network)—encryption (2D‐SLC hyperchaotic map)—hiding (matrix encoding). First, the advantages of deep learning are explored. It can compress the width, height, channel, and pixel values of the plain image simultaneously. Second, a new 2D‐SLC hyperchaotic map is designed to ensure security. It has a larger chaotic space and better randomness. Finally, to obtain a high‐quality cipher image, the secure secret image is hidden in the grey carrier image by matrix encoding. The scheme can compress and encrypt the grey or colour plain image and then hide it in a grey carrier image. In addition, the theoretical peak signal‐to‐noise ratio (PSNR) between the cipher image and the carrier image is improved from 40.9292 to 42.1785 dB. The total running time is only about 0.35, 0.87 and 3.1 s for a 256 × 256, 512 × 512 and 1024 × 1024 grey or colour plain image, respectively.

Reversible Data Hiding in Encrypted Image Based on Bit-Plane Redundancy of Prediction Error

In this paper, we propose a reversible data hiding scheme in an encrypted image based on bit-plane redundancy of prediction error. The scheme greatly improves the embedding capacity while maintaining lossless image recovery and error-free secret data extraction. Firstly, the original image is preprocessed to obtain the prediction error image. After the error matrix is divided into blocks, the corresponding block type is obtained. Secondly, the predicted error image is encrypted with stream cipher and the encryption matrix blocks are scrambled to ensure the security of the scheme. Finally, after embedding the block type value into the encrypted image, the spare room corresponding to each block was obtained, which was used to embed the secret data. The scheme makes full use of the spatial correlation of the pixels in the block, so it improves the embedding rate. By selecting 100 images in each dataset of BOSSbase and BOWS-2, when the block size is 3×3, the average embedding rate of our scheme can reach 3.56 bpp and 3.81 bpp, respectively. The performance of the proposed method is better than the other schemes with similar properties.

BSAF: A blockchain-based secure access framework with privacy protection for cloud-device service collaborations

In an open Internet-of-Things (IoT) environment, cloud-device collaborative development brings more functional services to users. However, privacy disclosure risks concerning service provision and users’ accessing behavior also increase. Traditional privacy protection approaches involve a centralized system with a third-party administrative server, which has the risk of single-point failures and overlooks the issue of privacy information leakage concerning users’ behaviors. Blockchain is a decentralized and traceable technology that provides a reliable solution for secure access by users. This paper proposes a novel blockchain-based secure access framework (BSAF) for cloud-device service collaborations with privacy protection. Specifically, a key matrix encryption mechanism is used to protect the privacy of users’ behaviors, and a fully homomorphic encryption mechanism is designed to protect the privacy of service content. Two smart contracts are designed: a request verification smart contract to verify the access rights of users, and a behavior punishment smart contract to audit users’ access behaviors. Comprehensive experiments on the Ethereum blockchain network show that the proposed BSAF framework outperforms existing schemes in latency reduction and cost saving, and is more suitable for low-profile IoT devices.

Anti-leakage method of network sensitive information data based on homomorphic encryption

Abstract With the development of artificial intelligence, people begin to pay attention to the protection of sensitive information and data. Therefore, a homomorphic encryption framework based on effective integer vector is proposed and applied to deep learning to protect the privacy of users in binary convolutional neural network model. The conclusion shows that the model can achieve high accuracy. The training is 93.75% in MNIST dataset and 89.24% in original dataset. Because of the confidentiality of data, the training accuracy of the training set is only 86.77%. After increasing the training period, the accuracy began to converge to about 300 cycles, and finally reached about 86.39%. In addition, after taking the absolute value of the elements in the encryption matrix, the training accuracy of the model is 88.79%, and the test accuracy is 85.12%. The improved model is also compared with the traditional model. This model can reduce the storage consumption in the model calculation process, effectively improve the calculation speed, and have little impact on the accuracy. Specifically, the speed of the improved model is 58 times that of the traditional CNN model, and the storage consumption is 1/32 of that of the traditional CNN model. Therefore, homomorphic encryption can be applied to information encryption under the background of big data, and the privacy of the neural network can be realized.

A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system-Fibonacci Q Matrix encryption.

Digital image watermarking, the process of marking a host image with a watermark, is generally used to authenticate the data. In the medical field, it is of utmost importance to verify the authenticity of the data using Medical Image Watermarking (MIW), especially in e-healthcare applications. Recently, MIW with image fusion, the merging of multimodal images to improve image quality, is being widely utilized to make diagnosis more accessible and precise with the verified data. This paper offers a durable and secure fusion-based hybrid MIW approach. The method initially used Fast Filtering (FF) to merge two medical images from different modalities to form the cover image. A first-level Redundant Discrete Wavelet Transform (RDWT) is employed on this host image to locate the component with the highest entropy. Then a single-level Discrete Wavelet Transform (DWT) is applied to it. It performed a Multi-resolution Singular Value Decomposition (MSVD) on the wavelet decomposed component and the embedding watermark. Finally, a Hyperchaotic System-Fibonacci Q Matrix (HFQM) encryption system was utilized, which increases the watermarked image's security. Here, using various medical images, the performance of the proposed technique is evaluated. Without any attacks, the approach achieved a maximum Peak Signal to Noise Ratio (PSNR) of 90.31 dB and a Structural Similarity Index Matrix (SSIM) of value 1. Various watermarking assaults were employed to test the proposed method's resilience. The suggested technique achieved a perfect value of 1 for the Normalised Correlation (NC) for almost all attacks with acceptable imperceptibility, which substantially improves over current procedures. The suggested technique's average embedding and extraction times are 0.3958 and 0.4721 seconds, respectively, which are pretty short compared to existing approaches.

A Novel Cipher-Based Data Encryption with Galois Field Theory

Both the act of keeping information secret and the research on how to achieve it are included in the broad category of cryptography. When people refer to "information security," they are referring to the study and use of methods that make data transfers harder to intercept. When we talk about "information security," this is what we have in mind. Using private keys to encrypt and decode messages is a part of this procedure. Because of its vital role in modern information theory, computer security, and engineering, cryptography is now considered to be a branch of both mathematics and computer science. Because of its mathematical properties, the Galois field may be used to encrypt and decode information, making it relevant to the subject of cryptography. The ability to encrypt and decode information is one such use. In this case, the data may be encoded as a Galois vector, and the scrambling process could include the application of mathematical operations that involve an inverse. While this method is unsafe when used on its own, it forms the foundation for secure symmetric algorithms like AES and DES when combined with other bit shuffling methods. A two-by-two encryption matrix is used to protect the two data streams, each of which contains 25 bits of binary information which is included in the proposed work. Each cell in the matrix represents an irreducible polynomial of degree 6. Fine-tuning the values of the bits that make up each of the two 25-bit binary data streams using the Discrete Cosine Transform (DCT) with the Advanced Encryption Standard (AES) Method yields two polynomials of degree 6. Optimization is carried out using the Black Widow Optimization technique is used to tune the key generation in the cryptographic processing. By doing so, we can produce two polynomials of the same degree, which was our original aim. Users may also use cryptography to look for signs of tampering, such as whether a hacker obtained unauthorized access to a patient's medical records and made any changes to them. Cryptography also allows people to look for signs of tampering with data. Indeed, this is another use of cryptography. It also has the added value of allowing users to check for indications of data manipulation. Users may also positively identify faraway people and objects, which is especially useful for verifying a document's authenticity since it lessens the possibility that it was fabricated. The proposed work achieves higher accuracy of 97.24%, higher throughput of 93.47%, and a minimum decryption time of 0.0047 s.