Permutation Operations Research Articles

CIE-LSCP: color image encryption scheme based on the lifting scheme and cross-component permutation

Encryption of meaningful images into unidentifiable noise-like images can improve the security of images during storage and transmission. In this paper, a novel color image encryption method based on the lifting scheme and cross-component permutation (CIE-LSCP) is introduced. First, original image is divided into red, green and blue components, and then the three components are processed by a preprocessing strategy based on the lifting scheme (PSLS) to change the statistical distribution of the pixels. Second, a block-based cross-component permutation based on index vectors (BCPIV) is presented to divide three preprocessed components into blocks and perform the cross-component permutation operation on them, and three random matrices are utilized to determine the target component, target block and target pixel position of the current pixel to be moved, respectively. Subsequently, a multi stochastic diffusion based on random sequences (MSDRS) is developed to alter the pixel values of the permutated components, and finally a ciphertext image is gotten by merging the three components. Moreover, the SHA256 hash values of the plaintext image are used to obtain the initial parameters of the chaotic system, and the obtained chaotic sequences are applied in the image encryption process. Wherein the generated random sequences are highly reliant on the plaintext image, making the encryption scheme resistant to both known-plaintext attacks and chosen-plaintext attacks. Experimental results demonstrate that the proposed scheme has good security and effectiveness and can be applied for secure transmission of digital images over the Internet.

A Simple and Fast Medical Image Encryption System Using Chaos-Based Shifting Techniques

Medical images such as brain MRI, ultrasound, and X-ray images are currently used by doctors to treat many serious diseases in most hospitals and clinics. These images play a major role in medical diagnosis. Diagnostic images with sensitive patient data are recorded and exchanged owing to the advancement of public networks. As a result, maintaining the privacy of sensitive patient data is becoming increasingly important. In medical imaging, the pixels are highly correlated. To reduce pixel correlation, a variety of complex encryption methods have been used in recent years, increasing the algorithm's computational complexity. This paper proposes an image encryption method based on chaos-based shifting operations. Security, convenience, and speed are all advantages of shifting activities. This method initially executes bit-plane flipping and permutation operations, then block-based flipping and diffusion operations to obtain the cipher images. The simulation and security analysis findings show that the proposed approach performs better.

Image encryption scheme based on blind signature and an improved Lorenz system

In this paper, a new improved Lorenz system (ImproLS) is designed with a better behavior due to a larger positive Lyapunov exponent. Then, an asymmetric image encryption scheme is presented based on blind signature and the ImproLS. First, the sender uses the public key from the receiver to produce the cipher keys in conjunction with the Rivest–Shamir–Adleman (RSA) algorithm. Then, a new mathematical model is established to convert both the plain keys and the cipher keys to generate the initial values for ImproLS. After doing preprocess to the secret plain image, its statistical characteristics is changed greatly. Then, by employing permutation and diffusion operations, the cipher image can be obtained and sent to the receiver and the signer. Second, the signer encrypts the digital signature image using Arnold transform in conjunction with the RSA algorithm, and applies the discrete wavelet transform (DWT) to the cipher image. The scrambled signature image is then embedded into the wavelet domain of cipher image blindly. Then, the cipher image containing signature is sent to the receiver for authentication. Third, the receiver uses the private key to check the signature and recover the cipher image. After comparing the decrypted images from both the sender and the signer, the receiver can judge the source of the secret plain image. Experimental results show that the information entropy is high and closer to the theoretical value. In addition, both unsigned and signed cipher images can effectively resist salt and pepper attack, and clipping attack.

Optimization and Design of Key Expansion of SM4 Algorithm Based on ZYNQ

As the application of "China Intelligent Manufacturing" in the field of production becomes more and more extensive, it becomes crucial to ensure the security of information interaction between production equipment. China's domestic encryption algorithm - SM4 block encryption algorithm came into being. The algorithm consists of a key expansion function and an encryption and decryption function. The key expansion function collects 128-bit keys at a time, and generates thirty-two 32-bit encryption keys after XOR operation, nonlinear permutation operation (S-box) and linear shift operation.The XOR operation parameter CK of the original key expansion function is obtained by the modulo method, which is similar to the system parameter FK and is a fixed value. In order to reduce the risk of deciphering caused by fixed parameters, the lightweight two-dimensional chaos algorithm Hemon is introduced for optimization design. The system parameters used in the SM4 algorithm are randomly generated with the change of the key, which improves the security of the algorithm. And use the hardware encryption platform ZYNQ-7000 series model xc7z100ffv900-1 FPGA side design and implementation, relying on the hard to decipher of hardware encryption to complete the optimization of SM4 algorithm key exchange.

Wave function identity: A new symmetry for 2-electron systems in an electromagnetic field

Stationary-state Schr{\"o}dinger-Pauli theory is a description of electrons with a spin moment in an external electromagnetic field. For 2-electron systems as described by the Schr{\"o}dinger-Pauli theory Hamiltonian with a symmetrical binding potential, we report a new symmetry operation of the electronic coordinates. The symmetry operation is such that it leads to the equality of the transformed wave function to the wave function. This equality is referred to as the Wave Function Identity. The symmetry operation is a two-step process: an interchange of the spatial coordinates of the electrons whilst keeping their spin moments unchanged, followed by an inversion. The Identity is valid for arbitrary structure of the binding potential, arbitrary electron interaction of the form $w(|{\bf{r}} - {\bf{r}}'|)$, all bound electronic states, and arbitrary dimensionality. It is proved that the exact wave functions satisfy the Identity. On application of the permutation operation for fermions to the identity, it is shown that the parity of the singlet states is even and that of triplet states odd. As a consequence, it follows that at electron-electron coalescence, the singlet state wave functions satisfy the cusp coalescence constraint, and triplet state wave functions the node coalescence condition. Further, we show that the parity of the singlet state wave functions about all points of electron-electron coalescence is even, and that of the triplet state wave functions odd. The Wave Function Identity and the properties on parity, together with the Pauli principle, are then elucidated by application to the $2$-dimensional $2$-electron `artificial atoms' or semiconductor quantum dots in a magnetic field in their first excited singlet $2^{1}S$ and triplet $2^{3} S$ states.

Quantum Encryption and Decryption in IBMQ Systems using Quantum Permutation Pad

We present a functioning implementation of Kuang et al.’s Quantum Permutation Pad (QPP) using the Qiskit developmental kit on the currently available International Business Machines (IBM) quantum computers. For this implementation, we use a pad with 28 2-qubit permutation gates that provides 128 bits of entropy. In this implementation, we divide the plaintext into blocks of 2-bits each. Each such block is encrypted one at a time. For any given block of plaintext, a quantum circuit is created with qubits initialized according to the given plaintext 2-bit block. The plaintext qubits are then acted on with 2-qubit permutation operators chosen from a 28- permutation QPP pad. Due to the inability to send qubits directly, the ciphertext qubits are measured and transmitted to the decrypting side over a classical channel. The decryption can be performed on either a classical or quantum computer. The decryption uses an inverse Quantum Permutation Pad with the Hermitian conjugates of the corresponding permutation gates used for the encryption. We are currently working on advancing the implementation of QPP to include additional steps for security and efficiency.

Non-Uniform Sparse Fourier Transform and Its Applications

The fast approximation algorithm of non-uniform discrete Fourier transform (NUDFT) is an important issue in signal processing. In this paper, a novel estimation algorithm is constructed for NUDFT-II, which is the general form of the sparse Fourier transform (SFT). Firstly, we propose the cyclic convolution in the non-uniform frequency domain and derive the product and convolution theorem. Secondly, the relationship is deduced between the inverse NUDFT of a uniformly sampled sequence in the frequency domain and that of a non-uniformly sampled sequence. Then, based on the relationship and cyclic convolution, we establish a random permutation operation, a non-uniform flat window filtering operation, and a frequency subsampling operation for NUDFT-II to compress sparse signal length. Meanwhile, a method to estimate the significant frequencies' locations and values is introduced. Finally, we propose the non-uniform sparse Fourier transform (NUSFT). Simulation results demonstrate that the proposed method has low complexity in sample and runtime, and has high robustness. Furthermore, the NUSFT algorithm has been applied to signal detection and reconstruction, which shows that the required signals are obtained accurately. And the NUSFT can detect frequencies that cannot be detected by the SFT.

New chaotic satellite image encryption by using some or all the rounds of the AES algorithm

ABSTRACT This study suggests new satellite chaotic image encryption by using some or all the rounds of the AES algorithm to improve security techniques to protect sensitive data and images during transmission between satellites and ground stations. First, the 7D hyperchaotic system generates the initial key of the pseudo-AES algorithm by using the first and the second variables of the hyperchaotic system. Second, the pseudo-AES algorithm consists of several rounds, where two of them are fixed rounds (the first and the last rounds of the classical AES algorithm), while the number of the remaining rounds depends on the value of the third variable of the hyperchaotic system which considered as a determiner. Third, the permutation operation is applied to the output of the pseudo-AES algorithm by an arrangement permutation on the rows and the columns using the fourth and fifth variables of the hyperchaotic system. Finally, the remaining two variables of the hyperchaotic system are used to generate a mask matrix to be XORed with the output of the previous step to obtain the final cipher image. Moreover, the experimental results show that the proposed technique of encryption for satellite images has extraordinary resistance against well-known attacks.

An efficient multimedia compression-encryption scheme using latin squares for securing Internet-of-things networks

The security of multimedia content and its applications have become a a very important task in digital communication technology. The extensive use of multimedia content over wireless networks makes the network applications exposed to different types of attacks. Cryptography is one of the main technologies that provide security and confidentiality to prevent unauthorised data access. A suitable design of an encryption system must be chosen to fully secure and maintain the privacy of data. On the other hand, the large size of data will be the prominent issue. In order to maintain a better performance of Internet-of-Things (IoT) applications, an appropriate compression-encryption scheme has to be used to ensure security and high speed without increasing the cryptosystem complexity in order to achieve a large communication capacity. Therefore, this paper presents a hybrid scheme of H.264/MVC compression and encryption process based on Latin Square Cipher (LSC) using symmetric keys. The Latin Square Whitening and Latin Square Substitution and Permutation operations are included in the encryption algorithm. The simulation results and analysis on different RGB video frames using MATLAB show that the proposed scheme achieves high protection, integrity, and security performance for different transmitted video frames. In addition, high robustness against different network attacks is achieved compared to the state-of-the-art algorithms.

PEWOBS: An efficient Bayesian network learning approach based on permutation and extensible ordering-based search

How to efficiently construct Bayesian network (BN) is a major research hotspot in the field of artificial intelligence. Ordering-based search (OBS) algorithm is a kind of search scoring-based BN structure learning algorithm with good performance. This kind of algorithm constructs the BN structure by optimizing the ordering of variables iteratively. However, it has the problems of poor initial ordering quality, insufficient ordering optimization and low accuracy. In order to solve these problems, this paper proposes a Bayesian network structure learning approach (PEWOBS) based on full permutation and extensible ordering-based search. It firstly uses the local learning and pruning method to process the dataset with small and large number of variables respectively, and obtains the candidate parent sets of each variable efficiently. Then it obtains the high quality of initial ordering in consideration of the global benefits and individual losses. On the basis of the initial optimization of ordering by the extensible window operator, the second optimization of sub-ordering is performed using the full permutation operator. The final BN structure with the largest score will be obtained according to the final ordering. Experimental results show that PEWOBS can get a better network structure in the first iteration. It has better network learning accuracy and efficiency compared with several kinds of existing OBS approaches.Availability and implementation: codes and experiment dataset are available at: http://122.205.95.139/PEWOBS/.

Systematic Polar Coded Modulation for Informed Receivers

The problem of coding for informed receivers (IR) is considered, where a transmitter multicasts a bunch of messages to its associated receivers, each of which already has a subset of messages as side information. Which receiver has what side information is unknown to the transmitter. A family of polar codes for IR was proposed for binary-input channels in (Huang and Shieh, 2018), which was shown to provide excellent side information gain regardless of side information configuration and content. A trial extension of this scheme to high-order modulations, however, was unsatisfactory. In this paper, two families of systematic polar coded modulations for IR are proposed. In the first family, by building a connection between the message bits and modulated symbols under systematic polar codes, we leverage the bit assignments and labeling techniques such that receiver side information can be directly translated to a minimum distance gain. In the second family, a permutation operation is added before modulation such that the message bits can be properly reordered to further advance and balance the gains among different side information configurations. Simulations show that the proposed schemes provide large and balanced side information gains and significantly outperform the system directly adapted from (Huang and Shieh, 2018) onto high-order modulations.

All-optical synthesis of an arbitrary linear transformation using diffractive surfaces

Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components. Here, we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (Ni) and output (No), where Ni and No represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary, and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is ≥Ni × No, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N < Ni × No. These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive surfaces.

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

Secure and Energy Efficient-Based E-Health Care Framework for Green Internet of Things

This paper proposes a secure and energy-efficient Internet of Things (IoT) model for e-health. The main objective is to secure the transmission and retrieval of biomedical images over IoT networks. To achieve this, the compressive sensing and five-dimensional hyper-chaotic map (FDHC) are utilized to encrypt the biomedical images. However, FDHC suffers from the hyper-parameter tuning problem. To resolve this, a θ-non-dominated sorting genetic algorithm III ( θ-NSGA-III) based FDHC is proposed. Initially, the initial attributes of FDHC are tuned using θ-NSGA-III. Then, the tuned attributes are used by FDHC to generate the secret keys. The obtained secret keys are then utilized to permute and diffuse the input biomedical images. Permutation and diffusion operations are applied row-wise and column-wise on the input biomedical image to perform encryption. The proposed technique is sensitive towards the input images as the permutation and diffusion of each row and column depends upon the initial scrambled row and column. Experimental results reveal that the proposed technique outperforms the state-of-art image encryption techniques. The proposed framework encrypts and decrypts images at a higher speed, therefore, it can be used to secure the communication in green IoT networks.

A Countermeasure of Power Attack for Lightweight Cryptographic Algorithm Using Bit Permutation Operation

The implantation security of cryptographic algorithm is very important. This paper proposes a method to resist power consumption attack for lightweight cryptographic algorithms. Using bit permutation operation as a countermeasure, we ensure that the position of each data of the original intermediate value data has changed after the bit is permutated, which greatly increases the difficulty of the attack. SKINNY is taken as an example for evaluate the effect of the proposed countermeasure. The experiment results show that the key will not reveal over 100,000 energy traces by using the countermeasure.

A new image encryption algorithm based on\xa0the OF-LSTMS and chaotic sequences

In this paper, a novel image encryption algorithm based on the Once Forward Long Short Term Memory Structure (OF-LSTMS) and the Two-Dimensional Coupled Map Lattice (2DCML) fractional-order chaotic system is proposed. The original image is divided into several image blocks, each of which is input into the OF-LSTMS as a pixel sub-sequence. According to the chaotic sequences generated by the 2DCML fractional-order chaotic system, the parameters of the input gate, output gate and memory unit of the OF-LSTMS are initialized, and the pixel positions are changed at the same time of changing the pixel values, achieving the synchronization of permutation and diffusion operations, which greatly improves the efficiency of image encryption and reduces the time consumption. In addition the 2DCML fractional-order chaotic system has better chaotic ergodicity and the values of chaotic sequences are larger than the traditional chaotic system. Therefore, it is very suitable to image encryption. Many simulation results show that the proposed scheme has higher security and efficiency comparing with previous schemes.

PFWNet: Pretraining neural network via feature jigsaw puzzle for weakly-supervised temporal action localization

Weakly supervised temporal action localization is a challenging yet interesting task. Existing methods usually apply a few temporal convolutional layers or linear layers to predict classification scores, where the model capacity is limited. Inspired by counterpart researches, increasing model capacity is the potential to improve the localization performance. However, under the weakly supervised paradigm, the video-level classification label is insufficient to learn large-capacity models. The essential reason lies in that most of the inputs to action localization networks are high-level features extracted by video recognition models. In lack of off-the-shelf initialization weights, the action localization networks have to train from scratch and can only explore low-capacity models. In this work, we are inspired by the self-supervised learning paradigm and propose to learn high-quality representative models via solving the feature jigsaw puzzle task. The proposed self-supervised pretraining process can explore networks with large kernel size and deeper layers, which can provide valuable initialization to action localization networks. In the implementation, we first discover potential action scopes via calculating motion intensity. Then, we cut features into snippets and permute them into out-of-order status. We randomly discard frames for boundaries between two snippets to guide the network learning high-level representations and prevent information leakage. Moreover, because the potential permutation number factorially rises with the increase of snippet number, we select a fixed number of permutation operations via the maximum hamming distance criterion, which eases the learning process. Comprehensive experiments on two benchmarks demonstrate the efficiency of pretraining to weakly supervised action localization task, and the proposed method builds new state-of-the-art performance.

Sensitive Label Privacy Preservation with Anatomization for Data Publishing

Data in its original form, however, typically contain sensitive information about individuals. Directly publishing raw data will violate the privacy of people involed. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Although algorithms based on generalization and suppression have been proposed to protect the sensitive attributes and resist these multiple types of attacks, they often suffer from large information loss by replacing specific values with more general ones. Alternatively, anatomization and permutation operations can de-link the relation between attributes without modifying them. In this paper, we propose a scheme Sensitive Label Privacy Preservation with Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes two procedures, table division and group division. During the table division, we adopt entropy and mean-square contingency coefficient to partition attributes into separate tables to inject uncertainty for reconstructing the original table. During the group division, all the individuals in the original table are partitioned into non-overlapping groups so that the published data satisfies the pre-defined privacy requirements of our ( $\alpha,\beta,\gamma,\delta$ α , β , γ , δ ) model. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility.

An Efficient Plaintext-Related Chaotic Image Encryption Scheme Based on Compressive Sensing.

With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the cloud applications. Facing the demands, an efficient and secure plaintext-related chaotic image encryption scheme is proposed based on compressive sensing for achieving the compression and encryption simultaneously. In the proposed scheme, the internal keys for controlling the whole process of compression and encryption is first generated by plain image and initial key. Subsequently, discrete wavelets transform is used in order to convert the plain image to the coefficient matrix. After that, the permutation processing, which is controlled by the two-dimensional Sine improved Logistic iterative chaotic map (2D-SLIM), was done on the coefficient matrix in order to make the matrix energy dispersive. Furthermore, a plaintext related compressive sensing has been done utilizing a measurement matrix generated by 2D-SLIM. In order to make the cipher image lower correlation and distribute uniform, measurement results quantified the 0∼255 and the permutation and diffusion operation is done under the controlling by two-dimensional Logistic-Sine-coupling map (2D-LSCM). Finally, some common compression and security performance analysis methods are used to test our scheme. The test and comparison results shown in our proposed scheme have both excellent security and compression performance when compared with other recent works, thus ensuring the digital image application in the network.

Bit-level image encryption algorithm based on BP neural network and gray code

In recent years, people have put forward various image encryption algorithms based on pixel level. In fact, bit level encryption has better effect than pixel level encryption. Therefore, this paper proposes a new bit-level image encryption algorithm based on Back Propagation (BP) neural network and Gray code. Firstly, the plaintext image is conversioned into binary image, then, the hyperchaotic Lorentz system is used to generate two sets of chaotic sequences for the Gray code bit-level permutation operation to generate the permutation matrix. Secondly, the permutation matrix is converted into a bit matrix reverse order output to generate a diffusion matrix. Finally, the algorithm uses a BP neural network composed of Logistic map and Piece-Wise Linear Chaotic (PWLCM) map to generate a key stream. The key stream is xored with the diffusion matrix to generate a ciphertext matrix. The experimental results show that the algorithm improves the encryption efficiency, has good security and can resist common attack methods.