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Chaos-based secured modified strong recovery conditions for least support orthogonal matching pursuit in the noisy case
... • Privacy-preserving techniques for IoT data collection and processing: Privacy-preserving techniques, such as differential privacy or secure multi-party computation, enable data analysis while preserving the privacy of individual users. Differential privacy introduces controlled noise or perturbation to data to provide aggregate insights without revealing sensitive individual information [90], [91]. Secure multi-party computation enables collaboration and data analysis across multiple parties without sharing raw data. ...
The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term ’security’ encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.
World has become a global village after introduction of social media and social networks. However, it extensively increased the demand for network resources, particularly multimedia traffic like images, videos and audio. The medium for this extensive traffic is always public networks such as internet or cellular networks. But the open nature of such network like internet always creates security threats for data during transmission. Due to many intrinsic features and higher correlation in multimedia traffic, existing encryption algorithms are not very convincing to perform well under critical scenarios. Therefore, many people in the research community are still working to propose new encryption schemes which can address these issues and handle multimedia traffic effectively on public networks. In this paper, we explore the weaknesses of existing encryption schemes, which compromise in many scenarios due to high correlation of multimedia traffic. To tackle this issue we proposed certain enhancements in an existing scheme. Our enhanced modification includes addition of bitwise XORed operation using non-linear chaotic algorithm. Performance of enhanced scheme is tested against state of the art security parameters. Efficiency of the proposed scheme is also validated via entropy, correlation, peak signal to noise ratio, unified average change intensity and number of pixels change rate tests.
This paper proposes a new fast matching pursuit technique named Partially Known Least Support Orthogonal Matching Pursuit (PKLS-OMP) which utilizes partially known support as a prior knowledge to reconstruct sparse signals from a limited number of its linear projections. The PKLS-OMP algorithm chooses optimum least part of the support at each iteration without need to test each candidate independently and incorporates prior signal information in the recovery process. We also derive sufficient condition for stable sparse signal recovery with the partially known support. Result shows that inclusion of prior information weakens the condition on the sensing matrices and needs fewer samples for successful reconstruction. Numerical experiments demonstrate that PKLS-OMP performs well compared to existing algorithms both in terms of reconstruction performance and execution time.
Out of various cryptographic attacks, Chosen-Plaintext Attack (CPA) is one of the most powerful and widely used attack on encrypted images. In order to efficiently resist such a strong attack, a novel chaos and compressive sensing based image encryption algorithm is presented in this work. Firstly, the original plaintext image is compressed via Orthogonal Matching Pursuit with Partially Known Support (OMP-PKS) and then the compressed image is confused and diffused using TD-ERCS and Skew-tent chaotic maps, respectively. Correlation among the compressed pixels is break down via confusing the image pixels using Tangent Delay Ellipse Reflecting Cavity Map System (TD-ERCS). Skew-tent chaotic map is employed for the pixel diffusion process. To get the final ciphertext image, the confused pixels are further changed through bitwise XORed operation via random matrix. For the sake of higher security, the initial conditions of chaotic maps are made dependent on the plaintext image and the parameters are computed via SHA-512. Furthermore, to decrease the transmission bandwidth, the measurement matrix is generated via Beta chaotic map. Instead of sending the whole measurement matrix, the sender will just send the Beta chaotic map initial conditions and control parameters (key) values along with the compressed ciphertext. The reliability and robustness of the designed image compression and encryption scheme are verified via experimental analysis and simulation results. All the experimental and simulation results are in favor of the proposed scheme.
The Subspace Pursuit (SP) algorithm is one of greedy pursuit methods which is used to reconstruct of K-sparse signal. Unlike existing condition produced by Dai and Milenkovic in 2004 that suggests the residual value of current iteration is reduced from the previous iteration, our approach eliminates useless information by reducing the number of iterations used to detect the correct support set. This operation is done by suggesting a new halting condition that can capture the best support set which can give the best representation of the reconstructed signal. The new halting conditions enhanced the SP algorithm to low computational complexity and reconstruction accuracy of the sparse signal.
A mathematically proven for two halt condition: noiseless setting, and noisy setting for signal affected by Gaussian noise. An error bound relation also is driven.
In this paper, we try also to relax the restricted isometry constant RIC value to narrows the gap between the known bounds and ultimate performance, which it produced by Dai.
Simulation results show that the new halting condition can overpass best results produce by earlier iteration and rise time consume. Our new halting condition can catch this earlier iteration and enhanced SP algorithm results.
Compressed sensing (CS) is a new field used for signal acquisition and design of sensor that made a large drooping in the cost of acquiring sparse signals. In this paper, new algorithms are developed to improve the performance of the greedy algorithms.
In this paper, a new greedy pursuit algorithm, SS-MSMP (Split Signal for Multiple Support of Matching Pursuit), is introduced and theoretical analyses are given. The SS-MSMP is suggested for sparse data acquisition, in order to reconstruct analog and efficient signals via a small set of general measurements. This paper proposes a new fast method which depends on a study of the behavior of the support indices through picking the best estimation of the corrosion between residual and measurement matrix.
The term multiple supports originates from an algorithm; in each iteration, the best support indices are picked based on maximum quality created by discovering correlation for a particular length of support.
We depend on this new algorithm upon our previous derivative of halting condition that we produce for Least Support Orthogonal Matching Pursuit (LS-OMP) for clear and noisy signal.
For better reconstructed results, SS-MSMP algorithm provides the recovery of support set for long signals such as signals used in WBAN.
Numerical experiments demonstrate that the new suggested algorithm performs well compared to existing algorithms in terms of many factors used for reconstruction performance.
In recent years, Compressed Sensing (CS) has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit (OMP) is fixed, thus limiting reconstruction precision. Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit (RIOMP) algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations. The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses. When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.
Greed is good. However, the tighter you squeeze, the less you have. In this paper, a less greedy algorithm for sparse signal reconstruction in compressive sensing, named orthogonal matching pursuit with thresholding is studied. Using the global 2-coherence, which provides a "bridge" between the well known mutual coherence and the restricted isometry constant, the performance of orthogonal matching pursuit with thresholding is analyzed and more general results for sparse signal reconstruction are obtained. It is also shown that given the same assumption on the coherence index and the restricted isometry constant as required for orthogonal matching pursuit, the thresholding variation gives exactly the same reconstruction performance with significantly less complexity.
A least support denoising-orthogonal matching pursuit (LSD-OMP) algorithm to reconstruct the sparse signal using less number of iterations from noisy measurements is presented. The algorithm achieves correct support recovery without requiring sparsity knowledge. An improved restricted isometry property-based condition is derived over the best-known results. Experimental results demonstrate that the LSD-OMP achieves good performance on recovering sparse signals, outperforming the latest state-of-the art method in terms of reconstructed signal-to-noise ratio and running time.
Chaos has been applied extensively in secure communication over the last decade, but most of the chaotic security protocols defined, are cryptographically weak or slow to compute. Also, study of chaotic phenomena as application in security area is not discussed in detail. In this paper, we have intensely studied chaos, their influence in secure communications and proposed a steganography technique in spatial domain for digital images based upon chaotic maps. By applying chaos effectively in secure communication, the strength of the overall anticipated algorithm has been increased to a significant level. In addition, few security statistical analyses such as correlation, entropy, energy, contrast, homogeneity, peak signal to noise ratio, and mean square error have also been carried out and shown that it can survive against various differential attacks such as the known message attack, known cover attack, known stego attack, and stego only attack.
Orthogonal matching pursuit (OMP) is a greedy search algorithm popularly being used for the recovery of compressive sensed sparse signals. In this correspondence, we show that if the isometry constant δK+1 of the sensing matrix Φ satisfies δK+1 <; 1/(1/√K+1) then the OMP algorithm can perfectly recover K-sparse signals from the compressed measurements y=Φx. Our bound offers a substantial improvement over the recent result of Davenport and Wakin and also closes gap between the recovery bound and fundamental limit over which the perfect recovery of the OMP cannot be guaranteed.
The paper shows that if a matrix has the restricted isometry property (RIP) of order with isometry constant and if its coherence is less than , then the Orthogonal Matching Pursuit (the Orthogonal Greedy Algorithm) is capable to exactly recover an arbitrary -sparse signal from the compressed sensing in at most iterations. As a result, an arbitrary -sparse signal can be recovered by the Orthogonal Matching Pursuit from measurements.
Bibliography: 23 titles.
We demonstrate a simple greedy algorithm that can reliably recover a vector v ?? ??d from incomplete and inaccurate measurements x = ?? v + e . Here, ?? is a N x d measurement matrix with N <<d, and e is an error vector. Our algorithm, Regularized Orthogonal Matching Pursuit (ROMP), seeks to provide the benefits of the two major approaches to sparse recovery. It combines the speed and ease of implementation of the greedy methods with the strong guarantees of the convex programming methods. For any measurement matrix ?? that satisfies a quantitative restricted isometry principle, ROMP recovers a signal v with O ( n ) nonzeros from its inaccurate measurements x in at most n iterations, where each iteration amounts to solving a least squares problem. The noise level of the recovery is proportional to ??{log n } || e ||2. In particular, if the error term e vanishes the reconstruction is exact.
As a greedy algorithm to recover sparse signals from compressed measurements,
the orthogonal matching pursuit (OMP) algorithm has received much attention in
recent years. In this paper, we introduce an extension of the orthogonal
matching pursuit (gOMP) for pursuing efficiency in reconstructing sparse
signals. Our approach, henceforth referred to as generalized OMP (gOMP), is
literally a generalization of the OMP in the sense that multiple indices are
identified per iteration. Owing to the selection of multiple "correct" indices,
the gOMP algorithm is finished with much smaller number of iterations compared
to the OMP. We show that the gOMP can perfectly reconstruct any K-sparse
signals (), provided that the sensing matrix satisfies the RIP with
. We also demonstrate by
empirical simulations that the gOMP has excellent recovery performance
comparable to -minimization technique with fast processing speed and
competitive computational complexity.
Compressed sensing is a developing field aiming at reconstruction of sparse
signals acquired in reduced dimensions, which make the recovery process
under-determined. The required solution is the one with minimum norm
due to sparsity, however it is not practical to solve the minimization
problem. Commonly used techniques include minimization, such as Basis
Pursuit (BP) and greedy pursuit algorithms such as Orthogonal Matching Pursuit
(OMP) and Subspace Pursuit (SP). This manuscript proposes a novel semi-greedy
recovery approach, namely A* Orthogonal Matching Pursuit (A*OMP). A*OMP
performs A* search to look for the sparsest solution on a tree whose paths grow
similar to the Orthogonal Matching Pursuit (OMP) algorithm. Paths on the tree
are evaluated according to a cost function, which should compensate for
different path lengths. For this purpose, three different auxiliary structures
are defined, including novel dynamic ones. A*OMP also incorporates pruning
techniques which enable practical applications of the algorithm. Moreover, the
adjustable search parameters provide means for a complexity-accuracy trade-off.
We demonstrate the reconstruction ability of the proposed scheme on both
synthetically generated data and images using Gaussian and Bernoulli
observation matrices, where A*OMP yields less reconstruction error and higher
exact recovery frequency than BP, OMP and SP. Results also indicate that novel
dynamic cost functions provide improved results as compared to a conventional
choice.
We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of LP optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter. In the noisy setting and in the case that the signal is not exactly sparse, it can be shown that the mean squared error of the reconstruction is upper bounded by constant multiples of the measurement and signal perturbation energies.