Existing privacy-preserving multi-keyword retrieval schemes often suffer from reduced retrieval efficiency, lack robust verification mechanisms in dynamic environments, and are prone to symmetric key leakage issues. To address these shortcomings, we propose a dynamic and secure multi-keyword search scheme with a two-factor verification mechanism, named Mimi. Specifically, Mimi first constructs a dynamic verification tree structure to accelerate the verification of the correctness of returned results. Second, it builds an encrypted searchable index that supports sub-linear search time complexity. Third, Mimi incorporates a secure symmetric key exchange protocol to protect the confidentiality of the symmetric key. Furthermore, Mimi supports multi-user search operations without increasing the index construction costs and accommodates dynamic updates to both user roles and data. Through comprehensive security analysis, we demonstrate that Mimi ensures the security of the encrypted searchable inverted index and maintains query indistinguishability for users. Empirical evaluations show that the Mimi scheme is efficient and effective.

Neural Architecture Search (NAS) has emerged as a crucial method for automating the design of deep learning models. Despite its potential, NAS frequently requires substantial computational and hardware resources. To mitigate these challenges, transferable NAS (TNAS) has been introduced, leveraging prior NAS results to enhance performance on new tasks. However, existing methods largely focus on knowledge transfer within identical neural search spaces, overlooking the potential for cross-domain transferability. Motivated by this gap, we explore evolutionary TNAS across heterogeneous search spaces by learning common neural representations. In particular, we introduce a novel approach that encodes both operational and topological information of neural architectures into a unified sequence using a simple tokenizer. This sequence is then processed by a variational auto-encoder, with a Transformer-based encoder to capture rich neural representations and a decoder that reconstructs the original sequence. By utilizing these latent representations, we further establish an inter-domain mapping that acts as a bridge, enabling effective explicit solution transfer among diverse search spaces to enhance the evolutionary NAS process. To harness this capability, we develop an evolutionary sequential transfer optimization approach that transfers knowledge during population initialization, providing both flexibility and adaptability. To the best of our knowledge, this work serves as the first attempt in the literature exploring evolutionary TNAS across diverse spaces. Moreover, we demonstrate the utility of our method through comprehensive empirical studies using different architecture spaces, including NAS-Bench-101, NAS-Bench-201, and the DARTS search space. Our results show that the proposed method significantly enhances the adaptability and performance of NAS across varied domains.

Existing CNN inference frameworks based on FHE often suffer from reduced efficiency and accuracy due to the polynomial approximation of activation functions, and they lack effective mechanisms to prevent sensitive information leakage during the final classification stage. To address these limitations, we propose FSAT, a fast and secure inference framework enhanced with adversarial training. Specifically, FSAT employs a private CNN model architecture, where linear layers are computed through an optimized homomorphic ciphertext convolution operation, while non-linear layer operations are efficiently realized using a secure searchable index and an encrypted look-up table, which replace polynomial activation approximations and significantly improve inference accuracy and latency performance. To further mitigate information leakage, we introduce a dual-constraint adversarial training scheme that makes it substantially more difficult for an adversary to infer sensitive attributes of the input data. Experimental results demonstrate that FSAT achieves high inference accuracy and efficiency while substantially reducing the risk of sensitive data leakage.

Deep neural networks (DNNs) are well-known to be susceptible to many universal adversarial perturbations (UAPs), where each UAP can successfully attack many images when added to the input. In this paper, we explore the existence of diversified UAPs, each of which successfully attacks a large but substantially different set of images. Since the sets of images successfully attacked by different UAPs are often complementary to each other, strategically selecting the most effective UAP to attack each new image could maximize the overall coverage of successful attacks. Following this insight, we propose a novel attack framework named boosting universal adversarial attack. The key idea is to simultaneously train a set of diversified UAPs and a selective neural network, such that the selective neural network can choose the most effective UAP when attacking a new target image. Due to the simplicity and effectiveness of the proposed boosting attack framework, it can be generally used to significantly boost the attack effectiveness of many classic single- UAP methods that only use a single UAP to attack all target images. Meanwhile, the boosting attack framework is also able to perform real-time attacks as it does not require any additional training or fine-tuning when attacking new target images. Extensive experiments demonstrate the outstanding performance of the proposed boosting attack framework.

Deep neural network (DNN)-based image watermarking models have been widely recognized as an effective way to manage the huge amount of AI-generated images. However, the vulnerability of such models to different forms of adversarial attacks has been a critical concern. Among the existing forms of attacks in the literature, image-dependent attacks cannot launch real-time attacks on a large number of watermarked images, because they need to train a new noise image to attack each new watermarked image; image-regeneration attacks either require a lot of information about the watermarking system or cause too much damage to the attacked image. To fill the gap in the existing forms of attacks, in this paper, we propose a novel form of attack named “fast and effective overwrite attack (FEOA)”, which achieves an extremely fast attack speed and strong attack effectiveness. In particular, we discovered a single noise image, when directly added to many watermarked images, can overwrite their true watermark messages to different ones in milliseconds. We also develop an adaptive version of FEOA, which trains$k$different noise images and applies the principle of divide and conquer to significantly improve attack effectiveness. Our work opens the door to quickly launching massive overwrite attacks on a large number of watermarked images, revealing a new robustness issue of DNN-based image watermarking models. Extensive experiments demonstrate the outstanding attack time efficiency and effectiveness of our methods.

Heterogeneous graph neural networks (HGNNs) have witnessed remarkable progress and widespread applications in recent years. Meanwhile, there is growing attention regarding their vulnerability to adversarial attacks. Existing attack methods for HGNNs generate perturbations to slightly modify the structure of a heterogeneous graph, thereby degrading the predictive performance of HGNNs on target nodes. However, to craft such a perturbation, these methods require solving a complicated optimization problem, which makes them computationally inefficient for launching attacks during the inference phase. In this work, we, therefore, introduce generative heterogeneous attack (GHAttack), a novel generative attack method for efficient and effective adversarial attacks on HGNNs. Specifically, GHAttack aims to train a perturbation generator, which produces a perturbation for each target node via a simple forward pass, while allowing the perturbation to modify edges on the heterogeneous relations of the graph to obtain high attack effectiveness. To achieve this, we design a novel model architecture for the generator, consisting of an HGNN backbone and a relation-aware output layer. We formulate the training of the generator as an optimization problem and efficiently solve it by addressing a series of technical challenges. Extensive experiments on ten representative HGNNs and six datasets verify the high efficiency and excellent effectiveness of GHAttack.

Federated Class-Incremental Learning (FCIL) enables dynamic model updates, but suffers from local and global catastrophic forgetting due to limited client storage and cross-client class non-i.i.d. issues. To address catastrophic forgetting, we propose a multi-scale FCIL scheme, DLA-FCIL, which incorporates a Double-Loss-assisted forgetting compensation mechanism and the Auxiliary generators based on data characteristics. Specifically, to mitigate local catastrophic forgetting, we incorporate an auxiliary generator on local clients for knowledge replay, augmenting the training datasets with generated samples to form hybrid datasets. Then, to fully leverage the hybrid datasets, we design a double-loss forgetting compensation mechanism. This mechanism includes a gradient-weighted compensation loss that normalizes forgetting rates across old class knowledge, and a semantic-transition compensation loss that extracts the semantic relationships between old and new classes, preventing abrupt shifts in semantic consistency during the class transitions. Besides, to effectively alleviate the catastrophic forgetting problem caused by global class imbalance, the trained auxiliary generator is sent to a proxy server with minimal communication cost to build an i.i.d. dataset, enabling the development of an optimal global model. Finally, comparison experiments on CIFAR100, ImageNet-Subset, and Tiny-ImageNet datasets demonstrate that DLA-FCIL consistently outperforms other FCIL baselines by approximately 3–15% in test accuracy.

The study of effective methods for removing image speckle remains a significant challenge in image processing. In contrast to additive noise, speckle noise is a multiplicative noise whose intensity is proportional to the signal. This results in a noise distribution that exhibits a high dependence on the signal intensity throughout the image, rendering it difficult to remove. Therefore, we present a novel approach to speckle noise removal using dynamical threshold-based fractional anisotropic diffusion (named as DTFAD) in this study. The method simultaneously considers both gradient and gray scale information in the image. In addition, the fractional derivative is integrated with anisotropic diffusion in the DTFAD model, which enhances the image denoising effect to preserve the fundamental features and edges of the image. The design of a dynamic threshold function in the diffusion coefficient enables the diffusion pattern and intensity to adaptively change according to image information, thus effectively removing speckle noise. We establish the well-posedness of the DTFAD model and implement it using an explicit finite difference scheme. Extensive experiments demonstrate that the DTFAD model outperforms traditional anisotropic diffusion techniques, and achieves a superior balance between denoising performance and texture preservation. This evidence demonstrates that the DTFAD model has the potential to be applied in practical engineering.

Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same feature representations from source domain to different target domains, resulting in poor transfer performance. To address this issue, we propose a novel subject-specific EEG recognition method named deep multiview module adaption transfer (DMV-MAT) network. First, we design a universal deep multiview (DMV) network to generate different types of discriminative features from multiple perspectives, which improves the generalization performance by extensive feature sets. Second, module adaption transfer (MAT) is designed to evaluate each module by the feature distributions of source and target samples, which can generate an optimal weight sharing strategy for each target subject and promote the model to learn domain-invariant and domain-specific features simultaneously. We conduct extensive experiments in two EEG recognition tasks, i.e., motor imagery (MI) and seizure prediction, on four datasets. Experimental results demonstrate that the proposed method achieves promising performance compared with the state-of-the-art methods, indicating a feasible solution for subject-specific EEG recognition tasks. Implementation codes are available at https://github.com/YangLibuaa/DMV-MAT.

This article studies bipartite time-varying formation-containment control (FCC) for large-scale multiagent systems (MASs) with unknown input of the leader. Large-scale systems are layered and grouped according to tasks to accomplish hierarchical bipartite time-varying FCC. It solves problems of strong coupling, communication redundancy, and collision avoidance in large-scale networks and enables many followers to implement new interactive behaviors in multiple convex hulls formed by leaders. First, for a constructed four-layer topology with group directed spanning tree, hierarchical bipartite time-varying FCC protocols with a collision avoidance term are designed to achieve collision-free bipartite group tracking control in a multiconvex hull. Then, appropriate control parameters are designed in protocols to promote effective information transmission between layers and groups. Compared with existing FCC strategies, hierarchical bipartite time-varying FCC strategies improve the control performance of MASs. Finally, the effectiveness of all protocols is proved through Lyapunov’s theorem and linear matrix inequality. The feasibility of protocols was verified through simulations.

Personalized federated learning (PFL) combines client needs and data characteristics to train personalized models for local clients. However, the most of previous PFL schemes encountered challenges such as low model prediction accuracy and privacy leakage when applied to practical datasets. Besides, the existing privacy protection methods fail to achieve satisfactory results in terms of model prediction accuracy and security simultaneously. In this paper, we propose a Privacy-preserving Personalized Federated Learning under Secure Multi-party Computation (SMC-PPFL), which can preserve privacy while obtaining a local personalized model with high prediction accuracy. In SMC-PPFL, noise perturbation is utilized to protect similarity computation, and secure multi-party computation is employed for model sub-aggregations. This combination ensures that clients' privacy is preserved, and the computed values remain unbiased without compromising security. Then, we propose a weighted sub-aggregation strategy based on the similarity of clients and introduce a regularization term in the local training to improve prediction accuracy. Finally, we evaluate the performance of SMC-PPFL on three common datasets. The experimental results show that SMC-PPFL achieves 2% ∼ 15% higher prediction accuracy compared to the previous PFL schemes. Besides, the security analysis also verifies that SMC-PPFL can resist model inversion attacks and membership inference attacks

The centralized server in traditional federated learning (FL) is authoritative (i.e. decisive control), which may cause immeasurable damage to the system's security in the event of decision failure or attack. To weaken the authority of the central server, existing studies have proposed blockchain-based federated learning (BFL) approaches. However, existing BFL still suffers from high resource overhead and difficulty in resisting high malicious ratio (more than 50%) attacks. To address the above challenge, this paper proposes an efficient and secure non-authoritative (i.e. highly decentralized) anarchic (i.e. distributed self-governance) federated learning framework which is named NAAFL. During the local process of NAAFL, an area credit-based screening mechanism for participating devices is proposed to ensure that participating devices are always highly trusted devices with higher total credit values. Then, to effectively exclude a high percentage of malicious training gradients, a multi-device validation voting mechanism based on historical information is designed to construct the global gradient. Subsequently, to weaken the central server authority and reduce the resource overhead while guaranteeing security, a secure and low-consumption consensus mechanism based on the federation chain is proposed, and the overhead is further reduced by a momentum acceleration algorithm. Finally, the theoretical analysis and experimental simulation are conducted on the proposed NAAFL. The results further show that the proposed NAAFL outperforms existing studies and can defend against attacks with up to 80% malicious ratio, which exceeds the common threshold (50%) of existing studies. Meanwhile, the overhead of NAAFL is reduced by about 77.51% compared to BFL.

Detecting and diagnosing major depressive disorder (MDD) is greatly crucial for appropriate treatment and support. In recent years, there have been efforts to develop automated methods for depression detection using machine learning techniques, which mainly analyze various data sources such as text, speech, and social media posts. However, the effectiveness and reliability of these methods may vary and more importantly, they fail to provide timely intervention and treatment to MDD patients. To address these challenges, we propose a novel electroencephalogram (EEG)-based MDD detection framework, which is named as multiview sparse dynamic graph convolution-based region-attention feature fusion network (MV-SDGC-RAFFNet). Specifically, we first design a multiview (MV) feature extractor to concurrently characterize EEG signals from temporal, spectral, and time-frequency views, providing rich semantic information on the emotional status of patients. Secondly, we introduce a sparse dynamic graph convolution network (SDGCN) to map the multidomain features into high-level representations, which avoids the limitation of over-smoothing and redundant edges existing in the conventional graph neural networks (GNNs). Finally, to efficiently fuse multidomain features, we propose a region-attention feature fusion network (RAFFNet), which applies different attention weights for brain regions and is greatly beneficial to boost the accuracy (ACC) of MDD detection. We validate the efficacy of the proposed MV-SDGC-RAFFNet framework on two public MDD datasets, and it achieves more promising detection performance against the state-of-the-art methods, indicating that our method has a prospect on clinical MDD detection.

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number of current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and a lack of effective result verification mechanisms. To address these limitations, we propose an adaptive verifiable privacy-preserving medical image retrieval (AVPMIR) scheme in the outsourced cloud. Specifically, we utilize the convolutional neural network (CNN) ResNet50 model to extract the feature of each medical image within the dataset of the medical institution, aiming to enhance retrieval accuracy. To enhance retrieval efficiency, we build an encryption searchable index based on a mini-batch$k$-means clustering algorithm. Furthermore, we present an index merging method in which multi-data owners build a different index tree according to different standards. To check the correctness of the returned results from the cloud server, we construct an adaptive verification framework for the obtained results based on chameleon hash and BLS signature. To provide strong security for the medical image datasets, we design an improved logistic chaotic mapping algorithm. The security analysis demonstrates that AVPMIR can defend various threat models. The experiment analysis further indicates that the AVPMIR can improve retrieval efficiency and demonstrate its practicability.

Blockchain is extensively applied to many fields as an immutable distributed ledger. However, the immutability contradicts regulations such as the GDPR ruling “the right to be forgotten” of data. Besides, numerous emerging blockchain-based applications call for elastic data management. To erase some data, redactable blockchains are proposed for breaking the immutability in a controlled way. Unfortunately, the prior solutions may suffer from poor security and centralized control of the redaction privilege. They cannot support dynamic nodes, where the departure of participators will result in a single point of failure. This paper proposes a noveldynamic and decentralizedattribute-basedchameleonhash (DACH) to make blockchain history mutable, achieving asecurely anddynamicallyredactable blockchain (SDR-chain) in a decentralized setting. We first propose the formal definition, security models, and concrete construction of our DACH. Meanwhile, we design a delegation algorithm of DACH to support a dynamically changing committee, where participators can freely and securely leave and join the network. Then, the transactions of the SDR-chain are redacted by computing DACH collisions. The security is analyzed in the random oracle model. Finally, theoretical analysis and experimental evaluation demonstrate that our SDR-chain is superior to the prior solutions in terms of security and functionality.

Recognizing visual objects from single-trial electroencephalograph (EEG) signals is a promising brain-computer interface technology. However, due to the redundant features from noisy multichannel EEG signals, it is still a challenging task to achieve high precision recognition. Recent deep learning approaches commonly extract spatio-temporal features of EEG signals, which neglect important spectral-temporal features and may degrade the EEG recognition performance. To address the deficiency, we propose a novel channel attention weighting and multilevel adaptive spectral aggregation based dual-branch spatio-temporal-spectral transformer feature fusion network (CAW-MASA-STST) for EEG-based visual recognition. Specially, we first develop a channel attention weighting (CAW) to automatically learn the channel weights of EEG signals. Then, a graph convolution-based MASA is employed to aggregate spectral-temporal features of different sub-bands. Finally, an STST is designed to fuse spatio-temporal and spectral-temporal features, which enhances the comprehensive learning ability by modeling the temporal dependencies of the fused features. Competitive experimental results on two public datasets demonstrate that the proposed method is able to achieve superior recognition performance compared with the state-of-the-art methods, indicating a feasible solution for visual recognition-based BCI technology. The code of our proposed method will be available athttps://github.com/ljbuaa/VisualDecoding.

In this brief, we investigate the limit cycle of a single-neuron system with smooth continuous and binary-value activation functions and its circuit design. By transforming the system into Liénard-type and using Poincaré-Bendixson theorem as well as the symmetry of these systems, we obtain the existence conditions of limit cycle of the system. Then, by comparing the integral value of the differential of positive definite function along two assumed limit cycles, we prove that the system cannot produce two coexisting limit cycles, which means that the system has at most one limit cycle. In addition, according to the two specific functions, i.e., smooth continuous and binary-value activation functions of the system, we give the numerical simulation and realize the circuit design of the single-neuron system by using Multisim modeling, respectively. The waveform diagram and phase diagram of the numerical simulation and circuit simulation are obtained. By comparing the results of numerical and circuit simulation, the effectiveness of our mathematical analysis and the feasibility of circuit design are better illustrated.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

High-performance traffic flow prediction models provide reliable future road information and optimize traffic navigation systems. However, the traffic data used for model learning contains lots of private information, and the existing privacy-preserving strategies always reduce the accuracy of prediction models. Besides, an effective traffic flow prediction model needs to be continuously and rapidly updated to adapt to dynamic changes in the traffic environment. Thus, we propose a Secure and Efficient Continuous Learning Model (SE-CLM) based on broad learning, spatial correlation, and adaptive sampling processing techniques to realize accurate and efficient traffic flow prediction under strong privacy protection. Specifically, SE-CLM is constructed on the broad network architecture to enable fast and continuous model training. This model is trained on a cloud server by combining the spatial correlation of traffic flows, to achieve accurate traffic flow prediction. Besides, an adaptive sampling strategy is designed to further improve the prediction accuracy of the model under the protection with differential privacy (DP), where the budget allocation for DP is optimized by adaptively sampling traffic flows with different timestamps for noise perturbation processing. Furthermore, the experimental simulations are conducted in real vehicular mobility datasets. The experimental results show that the designed spatial-based SE-CLM achieve more accurate and efficient traffic flow prediction than those of the other existing schemes. The adaptive sampling strategy not only significantly reduces the DP-noise added in traffic flows but also a 20% reduction in communication volume compared to other strategies. Finally, the security analysis also verifies that SE-CLM satisfies w-event ε-DP.

Images captured under low-light conditions are characterized by lower visual quality and perception levels than images obtained in better lighting scenarios. Studies focused on low-light enhancement techniques seek to address this dilemma. However, simple image brightening results in significant noise, blurring, and color distortion. In this paper, we present a low-light enhancement (LLE) solution that effectively synergizes Retinex theory with deep learning. Specifically, we construct an efficient image gradient map estimation module based on convolutional networks that can efficiently generate noise-free image gradient maps to assist with denoising. Second, to improve upon the traditional optimization model, we design a matrix-preserving optimization method (MPOM) coupled with deep learning modules, and it exhibits high speed and low memory consumption. Third, we incorporate image structure, image texture, and implicit prior information to optimize the enhancement process for low-light conditions and overcome prevailing limitations, such as oversmoothing, significant noise, and so forth. Through extensive experiments, we show that our approach has notable advantages over the existing methods and demonstrate superiority and effectiveness, surpassing the state-of-the-art methods by an average of 1.23 dB in PSNR for the LOL and VE-LOL datasets. The code for the proposed method is available in a public repository for open-source use: https://github.com/luxunL/DRNet .

With the widespread use of cloud storage technology by individuals and organizations, data providers usually send their data to cloud for storage to reduce memory pressure, and allow the users to retrieve these data, which has become the trend of rapid data retrieval. To guarantee the data confidentiality, several research works have been developed on encrypted cloud data for ranked multi-keyword retrieval. Nevertheless, most of these schemes are disabled since they cannot resist keyword guessing attacks. Moreover, the ranked top-$K$search results obtained by the subscriber from the encrypted cloud data are inaccurate. To overcome these drawbacks, we design a novel and efficient privacy-preserving ranked multi-keyword retrieval scheme (named as PRMKR) in this paper. With PRMKR, the data and the inverted indexes which belong to the data provider can be securely transferred to the cloud server. In addition, a registered subscriber can request accurate retrieval services without compromising his/her trapdoor information to the cloud server. Specifically, we design an encryption searchable plugin-in server and lower dimensional inverted indexesvector for data owners, which can further guarantee data confidentiality of the data owner and improve search efficiency, respectively. Our rigorous security proof demonstrates that PRMKR can withstand keyword guessing attacks. Finally, experimental evaluations confirm that PRMKR has decent computational and communication efficiency.

In recent years, Riemannian geometry, as an import- ant tool for designing and analyzing continuous trajectory flows, has been widely applied to solve nonlinear programming problems due to its ability to inspire the design of a new class of approaches for solving such problems. We first review some properties of invariant Riemannian metrics, then investigate the diagonal metric defined in general on the manifold$\mathbb {R}_{+ +}^{n}, $some geometric properties are derived, and finally a class of neurodynamic flow approaches are also proposed. The global existence and feasibility results of the produced neurodynamic flow approaches are obtained and the results can be further extended to a more universal smooth function. The asymptotical behaviors of proposed neurodynamic flow approaches are also analyzed and studied, and global convergence of the proposed neurodynamic flow approaches for seeking the minimum point of the convex and quasi-convex minimum problems on the non-negative orthant domain is developed. In addition, the derived convergence results can directly be adapted to the competitive Cohen–Grossberg neural networks as well as to image and signal processing in compressive sensing. The correctness and superiority of our proposed neurodynamic flow approaches are verified by some sparse signal and image reconstruction examples.

Data trading is an effective way for commercial companies to obtain massive personal data to develop their data-driven businesses. However, when data owners may want to sell their data without revealing privacy, data consumers also face the dilemma of high purchase costs due to purchasing too much invalid data. Therefore, there is an urgent need for a data trading scheme that can protect personal privacy and save expenses simultaneously. In this paper, we design a privACy-preserving and praCtical aggrEgateStatiStic trading scheme (named as ACCESS). Technically, we focus on the group-level pricing strategy to make ACCESS easier to implement. The differential privacy technique is applied to protect the data owners' privacy, and the sampling algorithm is adopted to reduce the data consumers' costs. Specifically, to provide a maximum tolerant privacy loss guarantee for the data owners, we design a decision algorithm to detect whether a conflict occurs between the consumer-specified accuracy level and the maximum tolerable privacy loss budget. Besides, to minimize the purchase cost for the data brokers, we develop a sampling-based aggregation method consisting of two sampling algorithms (called as BUSA and BKSA, respectively). BUSA enables reducing purchase costs with no additional background knowledge. Once the data broker knows the data boundary, BKSA can significantly reduce the amount of data that needs to be purchased, thereby the purchase cost is reduced. Rigorous theoretical analysis and extensive experiments (over four real-world and public datasets) further demonstrate the practicability of ACCESS.

We propose a full-reference image quality assessment (FR-IQA) method by incorporating low-level and high-level image features. First, in contrast to the preexisting deep IQA methods, which only use the features extracted by the deep network, we not only use the image gradient to replace the low-level features in the first two stages of the deep network, but also combine them with the middle-stage features of the deep network to construct the new low-level features. The deep features of shallow layers contain some unwanted noise and further result in a decline in IQA performance. Second, we combine the global features extracted by the self-attention-based model with the semantic features extracted by the convolutional neural network to form the high-level features. Instead of directly using the self-attention-based model trained on the classification task, we first train a no-reference (NR) IQA regression model on a larger dataset and then use the global features of this NR-IQA model. The self-attention-based model can capture the internal connections of the image and is more effective in extracting global information due to its larger perceptual field. In the final pooling stage, we combine the average pooling and the standard deviation pooling to obtain the dispersion and concentration of the similarity maps for a more comprehensive description of quality. Experiments show that our FR-IQA method is able to obtain competitive results on three standard IQA datasets.

This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine and closed, convex set constraints. In the smooth case, an accelerated primal-dual mirror dynamical approach (APDMD) based on accelerated mirror descent and primal-dual framework is proposed and accelerated convergence properties of primal-dual gap, feasibility measure and the objective function value along with trajectories of APDMD are derived by the Lyapunov analysis method. Then, we extend APDMD into two distributed dynamical approaches to deal with two types of distributed smooth optimization problems, i.e., distributed constrained consensus problem (DCCP) and distributed extended monotropic optimization (DEMO) with accelerated convergence guarantees. Moreover, in the nonsmooth case, we propose a smoothing accelerated primal-dual mirror dynamical approach (SAPDMD) with the help of smoothing approximation technique and the above APDMD. We further also prove that primal-dual gap, objective function value and feasibility measure along with trajectories of SAPDMD have the same accelerated convergence properties as APDMD by choosing the appropriate smooth approximation parameters. Later, we propose two smoothing accelerated distributed dynamical approaches to deal with nonsmooth DEMO and DCCP to obtain accelerated and efficient solutions. Finally, numerical and comparative experiments are given to demonstrate the effectiveness and superiority of the proposed accelerated mirror dynamical approaches.

With the development of wireless communications and the pervasiveness of location-aware mobile electronic devices, location-based services (LBS) which can provide a convenient lifestyle for people have attracted considerable interest recently. However, there still exists the privacy disclosure problem of LBS today. To solve this problem, in this article, we present a novel privacy-preserving LBS search scheme in outsourced cloud. In the proposed LBS search scheme, the LBS providers data are first outsourced to the cloud server in an encrypted method. Then, a registered user constructs a query model to obtain accurate LBS query results without divulging his/her location information and query attribute to the LBS provider and the cloud server. Specifically, based on the designed matrix encryption technology, the LBS search scheme can achieve privacy preservation of users query and confidentiality of LBS data in the outsourced cloud server. Through security analysis, we show that our scheme can resist various known security threats. The experimental results further show that our LBS search scheme greatly reduces the communication overhead and provides convenient search experience to the users.

Data deduplication can efficiently eliminate data redundancies in cloud storage and reduce the bandwidth requirement of users. However, most previous schemes depending on the help of a trusted key server (KS) are vulnerable and limited because they suffer from revealing information, poor resistance to attacks, great computational overhead, etc. In particular, if the trusted KS fails, the whole system stops working, i.e., single-point-of-failure. In this article, we propose aSecure andEfficient dataDeduplication scheme (named SED) in a JointCloud storage system which provides the global services via collaboration with various clouds. SED also supports dynamic data update and sharing without the help of the trusted KS. Moreover, SED can overcome the single-point-of-failure that commonly occurs in the classic cloud storage system. According to the theoretical analyses, our SED ensures the semantic security in the random oracle model and has strong anti-attack ability such as the brute-force attack resistance and the collusion attack resistance. Besides, SED can effectively eliminate data redundancies with low computational complexity and communication and storage overhead. The efficiency and functionality of SED improves the usability in client-side. Finally, the comparing results show that the performance of our scheme is superior to that of the existing schemes

Vision-based practical applications, such as consumer photography and automated driving systems, greatly rely on enhancing the visibility of images captured in night-time environments. For this reason, various image enhancement algorithms (EHAs) have been proposed. However, little attention has been given to the quality evaluation of enhanced night-time images. In this paper, we conduct the first dedicated exploration of the subjective and objective quality evaluation of enhanced night-time images. First, we build an enhanced night-time image quality (EHNQ) database, which is the largest of its kind so far. It includes 1,500 enhanced images generated from 100 real night-time images using 15 different EHAs. Subsequently, we perform a subjective quality evaluation and obtain subjective quality scores on the EHNQ database. Thereafter, we present an objective blind quality index for enhanced night-time images (BEHN). Enhanced night-time images usually suffer from inappropriate brightness and contrast, deformed structure, and unnatural colorfulness. In BEHN, we capture perceptual features that are highly relevant to these three types of corruptions, and we design an ensemble training strategy to map the extracted features into the quality score. Finally, we conduct extensive experiments on EHNQ and EAQA databases. The experimental and analysis results validate the performance of the proposed BEHN compared with the state-of-the-art approaches. Our EHNQ database is publicly available for download athttps://sites.google.com/site/xiangtaooo/.

Clinically, retinal vessel segmentation is a significant step in the diagnosis of fundus diseases. However, recent methods generally neglect the difference of semantic information between deep and shallow features, which fail to capture the global and local characterizations in fundus images simultaneously, resulting in the limited segmentation performance for fine vessels. In this article, a global transformer (GT) and dual local attention (DLA) network via deep-shallow hierarchical feature fusion (GT-DLA-dsHFF) are investigated to solve the above limitations. First, the GT is developed to integrate the global information in the retinal image, which effectively captures the long-distance dependence between pixels, alleviating the discontinuity of blood vessels in the segmentation results. Second, DLA, which is constructed using dilated convolutions with varied dilation rates, unsupervised edge detection, and squeeze-excitation block, is proposed to extract local vessel information, consolidating the edge details in the segmentation result. Finally, a novel deep-shallow hierarchical feature fusion (dsHFF) algorithm is studied to fuse the features in different scales in the deep learning framework, respectively, which can mitigate the attenuation of valid information in the process of feature fusion. We verified the GT-DLA-dsHFF on four typical fundus image datasets. The experimental results demonstrate our GT-DLA-dsHFF achieves superior performance against the current methods and detailed discussions verify the efficacy of the proposed three modules. Segmentation results of diseased images show the robustness of our proposed GT-DLA-dsHFF. Implementation codes will be available on https://github.com/YangLibuaa/GT-DLA-dsHFF.

Federated learning (FL) enables multiple clients to jointly train a global learning model while keeping their training data locally, thereby protecting clients’ privacy. However, there still exist some security issues in FL, e.g., the honest-but-curious servers may mine privacy from clients’ model updates, and the malicious clients may launch poisoning attacks to disturb or break global model training. Moreover, most previous works focus on the security issues of FL in the presence of only honest-but-curious servers or only malicious clients. In this paper, we consider a stronger and more practical threat model in FL, where the honest-but-curious servers and malicious clients coexist, named as the non-fully trusted model. In the non-fully trusted FL, privacy protection schemes for honest-but-curious servers are executed to ensure that all model updates are indistinguishable, which makes malicious model updates difficult to detect. Toward this end, we present an Adaptive Privacy-Preserving FL (Ada-PPFL) scheme with Differential Privacy (DP) as the underlying technology, to simultaneously protect clients’ privacy and eliminate the adverse effects of malicious clients on model training. Specifically, we propose an adaptive DP strategy to achieve strong client-level privacy protection while minimizing the impact on the prediction accuracy of the global model. In addition, we introduce DPAD, an algorithm specifically designed to precisely detect malicious model updates, even in cases where the updates are protected by DP measures. Finally, the theoretical analysis and experimental results further illustrate that the proposed Ada-PPFL enables client-level privacy protection with 35% DP-noise savings, and maintains similar prediction accuracy to models without malicious attacks.

Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In this paper, we challenge the robustness of existing deep hiding schemes by preventing the recovery of secret images, building on our in-depth study of state-of-the-art deep hiding schemes and their vulnerabilities. Leveraging our analysis, we first propose a simple box-free removal attack on deep hiding that does not require any prior knowledge of the deep hiding schemes. To improve the removal performance on the deep hiding schemes that may be enhanced by adversarial training, we further design a more powerful removal attack, efficient box-free removal attack (EBRA), which employs image inpainting techniques to remove secret images from container images. In addition, to ensure the effectiveness of our attack and preserve the fidelity of the processed container images, we design an erasing phase based on the locality of deep hiding to remove secret information and then make full use of the visual information of container images to repair the erased visual content. Extensive evaluations show our method can completely remove secret images from container images with negligible impact on the quality of container images.

Facial expression recognition (FER) in the wild is a challenging task for affective computing in human–machine interaction fields. However, most of the existing methods fail to learn the most prominent regions of facial images by simple cross-entropy loss due to the imbalance problem commonly existing in FER datasets, which limits the robustness and interpretability of the model. In addition, these methods only capture local features of original images with multisize shallow convolution and ignore facial texture characteristics, leading to a suboptimal recognition performance. To address these issues, in this article, we propose a novel FER network, named the attention-rectified and texture-enhanced cross-attention transformer feature fusion network (AR-TE-CATFFNet). Specifically, an attention-rectified convolution block is first designed to assist multiple convolution heads to focus on the critical areas of human faces and improve the model generalization. Second, we investigate a texture enhancement block to capture texture features through local binary pattern and gray-level co-occurrence matrix, which solves the limitation of insufficient texture information. Finally, a cross-attention transformer feature fusion block is employed to deeply integrate red, green, blue (RGB) features and texture features globally, which is beneficial to boost the accuracy of recognition. Competitive experimental results on three public datasets validate the efficacy of the proposed method, indicating that our proposed method achieves superior classification performance of 89.50% on real-world affective faces database (RAF-DB) dataset, 65.66% on AffectNet dataset, and 74.84% on FER2013 dataset against the existing methods.

Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting with it many times and producing global perturbations. However, all pixels are not equally crucial to the target model; thus, indiscriminately treating all pixels will increase query overhead inevitably. In addition, existing black-box attacks take clean samples as start points, which also limits query efficiency. In this article, we propose a novel black-box attack framework, constructed on a strategy of dual transferability (DT), to perturb the discriminative areas of clean examples within limited queries. The first kind of transferability is the transferability of model interpretations. Based on this property, we identify the discriminative areas of clean samples for generating local perturbations. The second is the transferability of adversarial examples, which helps us to produce local pre-perturbations for further improving query efficiency. We achieve the two kinds of transferability through an independent auxiliary model and do not incur extra query overhead. After identifying discriminative areas and generating pre-perturbations, we use the pre-perturbed samples as better start points and further perturb them locally in a black-box manner to search the corresponding adversarial examples. The DT strategy is general; thus, the proposed framework can be applied to different types of black-box attacks. We conduct extensive experiments to show that, under various system settings, our framework can significantly improve the query efficiency of existing black-box attacks and attack success rates.

In this article, a single-layer projection neural network based on penalty function and differential inclusion is proposed to solve nonsmooth pseudoconvex optimization problems with linear equality and convex inequality constraints, and the bound constraints, such as box and sphere types, in inequality constraints are processed by projection operator. By introducing the Tikhonov-like regularization method, the proposed neural network no longer needs to calculate the exact penalty parameters. Under mild assumptions, by nonsmooth analysis, it is proved that the state solution of the proposed neural network is always bounded and globally exists, and enters the constrained feasible region in a finite time, and never escapes from this region again. Finally, the state solution converges to an optimal solution for the considered optimization problem. Compared with some other existing neural networks based on subgradients, this algorithm eliminates the dependence on the selection of the initial point, which is a neural network model with a simple structure and low calculation load. Three numerical experiments and two application examples are used to illustrate the global convergence and effectiveness of the proposed neural network.

In this article, we consider a class of decentralized resource allocation problems over directed networks, where each node only communicates with its in-neighbors and attempts to minimize its own cost when network-wide resource constraints as well as local capacity limits are satisfied. Decentralized optimization to solve this problem has been a significant focus within engineering research due to its advantages in scalability, robustness, and flexibility. Most existing methods are synchronous while few works are devoted to asynchronously solving the problem. The problem becomes even more challenging when the networks are directed. To address the resource allocation problem when the above issues are considered, we propose a novel decentralized asynchronous algorithm based on the gossip-based communication protocol and epigraph strategy. An important feature of the algorithm is that it is implemented in a completely decentralized manner in the case of asynchronous communication and directed networks. We provide theoretical proof to guarantee the convergence of the proposed algorithm, which indicates that it can successfully allocate the optimal resource. When solving the resource allocation problem over time-varying directed networks, we further discuss a related decentralized asynchronous algorithm according to the random sleep protocol. Numerical examples are given to demonstrate the viability and performance of the algorithms.

The globally fixed-time stabilization and$H_{\infty }$control for the port-Hamiltonian differential algebraic equation (PH-DAE) is addressed by applying the structural properties of the port-Hamiltonian (PH) systems, and the interconnection and damping assignment passivity-based control (IDA-PBC) technique. For fixed-time control design of the differential algebraic equation systems, the main obstacle lies that the algebraic variables are hidden in the system and their dynamics are ambiguous. We propose an available and effective approach to overcome this obstacle. The system variables are decomposed into differential component and the algebraic component, and the explicit representation of the dynamics of algebraic component is obtained. Fist, we investigate the locally fixed-time stabilization control as well as globally attractive for PH-DAE. Second, we focus on solving the globally fixed-time stabilization and$H_{\infty }$control problems by jointly taking advantage of the locally fixed-time stabilization control for the origin and the globally fixed-time attractivity control for a predeterminate region. Finally, in order to address fixed-time stabilization control and$H_{\infty }$control for PH-DAE, new controllers are designed. Moreover, the convergence time of systems can be easily estimated after external disturbance vanishes. The efficiency of the theoretical results acquired in present article is verified via two examples with numerical simulation.

This article considers constrained nonsmooth generalized convex and strongly convex optimization problems. For such problems, two novel distributed smoothing projection neurodynamic approaches (DSPNAs) are proposed to seek their optimal solutions with faster convergence rates in a distributed manner. First, we equivalently transform the original constrained optimal problem into a standard smoothing distributed problem with only local set constraints based on an exact penalty and smoothing approximation methods. Then, to deal with nonsmooth generally convex optimization, we propose a novel DSPNA based on continuous variant of Nesterov’s acceleration (called DSPNA-N), which has a faster convergence rate$\mathcal {O} ({1}/{t^{2}})$, and we design a novel DSPNA inspired by the continuous variant of Polyak’s heavy ball method (called DSPNA-P) to address the nonsmooth strongly convex optimal problem with an explicit exponential convergent rate. In addition, the existence, uniqueness, and feasibility of the solution of our proposed DSPNAs are also provided. Finally, numerical results demonstrate the effectiveness of DSPNAs.

This letter develops a novel fixed-time stable neurodynamic flow (FTSNF) implemented in a dynamical system for solving the nonconvex, nonsmooth model L1-β2, β∈[0,1] to recover a sparse signal. FTSNF is composed of many neuron-like elements running in parallel. It is very efficient and has provable fixed-time convergence. First, a closed-form solution of the proximal operator to model L1-β2, β∈[0,1] is presented based on the classic soft thresholding of the L1-norm. Next, the proposed FTSNF is proven to have a fixed-time convergence property without additional assumptions on the convexity and strong monotonicity of the objective functions. In addition, we show that FTSNF can be transformed into other proximal neurodynamic flows that have exponential and finite-time convergence properties. The simulation results of sparse signal recovery verify the effectiveness and superiority of the proposed FTSNF.

Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose aSecure andEfficientDataMiningOutsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud’s ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.

This article discusses the couple-group consensus for heterogeneous multiagent systems via event-triggered and pinning control methods. Considering cooperative-competitive interaction among the agents, a novel group consensus protocol is designed. As inducing the time-correlation threshold function, a class of fully distributed event-triggered conditions without depending on any global information is proposed. Utilizing the Lyapunov stability theory, some sufficient conditions are obtained. Under hybrid event triggered and pinning control, pinning control strategies are first introduced. It is shown that under the proposed strategies, all agents can asymptotically achieve pinning couple-group consensus with discontinuous communication in a fully distributed way. Furthermore, the Zeno behavior for each agent is overcome. Finally, the reduction of the systems' controller update frequency and the correctness of our conclusions are illustrated by some simulations.

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both the characterizations in EEGs themselves and correlations among different electrodes simultaneously, generally neglect the spatial or temporal dependencies in an epileptic brain and, thus, produce suboptimal seizure prediction performance consequently. To tackle this issue, in this article, a patient-specific EEG seizure predictor is proposed by using a novel spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme (STS-HGCN-AL). Specifically, since the epileptic activities in different brain regions may be of different frequencies, the proposed STS-HGCN-AL framework first infers a hierarchical graph to concurrently characterize an epileptic cortex under different rhythms, whose temporal dependencies and spatial couplings are extracted by a spectral-temporal convolutional neural network and a variant self-gating mechanism, respectively. Critical intrarhythm spatiotemporal properties are then captured and integrated jointly and further mapped to the final recognition results by using a hierarchical graph convolutional network. Particularly, since the preictal transition may be diverse from seconds to hours prior to a seizure onset among different patients, our STS-HGCN-AL scheme estimates an optimal preictal interval patient dependently via a semisupervised active learning strategy, which further enhances the robustness of the proposed patient-specific EEG seizure predictor. Competitive experimental results validate the efficacy of the proposed method in extracting critical preictal biomarkers, indicating its promising abilities in automatic seizure prediction.

These days, the increasing incremental cost consensus-based algorithms are designed to tackle the economic dispatch (ED) problem in smart grids (SGs). However, one principal obstruction lies in privacy disclosure for generators and consumers in electricity activities between supply and demand sides, which may bring great losses to them. Hence, it is extraordinarily essential to design effective privacy-preserving approaches for ED problems. In this article, we propose a two-phase distributed and effective heterogeneous privacy-preserving consensus-based (DisEHPPC) ED scheme, where a demand response (DR)-based framework is constructed, including a DR server, data manager, and a set of local controllers. The first phase is that Kullback-Leibler (KL) privacy is guaranteed for the privacy of consumers' demand by the differential privacy method. The second phase is that (ε,δ) -privacy is, respectively, achieved for the generation energy of generators and the sensitivity of electricity consumption to electricity price by designing the privacy-preserving incremental cost consensus-based (PPICC) algorithm. Meanwhile, the proposed PPICC algorithm tackles the formulated ED problem. Subsequently, we further carry out the detailed theoretical analysis on its convergence, optimality of final solution, and privacy degree. It is found that the optimal solution for the ED problem and the privacy preservation of both supply and demand sides can be guaranteed simultaneously. By evaluation of a numerical experiment, the correctness and effectiveness of the DisEHPPC scheme are confirmed.

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the$L_{1}$-minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange method and the Lagrange method with derivative feedback and projection operator. Then, the optimality and global convergence of them are derived. In addition, considering that the collective neurodynamic approaches have the function of information protection and distributed information processing, first, under mild conditions, we transform the$L_{1}$-minimization problem into two network optimization problems. Later, two collective neurodynamic approaches based on the above centralized neurodynamic approaches and multiagent consensus theory are proposed to address the obtained network optimization problems. As far as we know, this is the first attempt to use the collective neurodynamic approaches to deal with the$L_{1}$-minimization problem in a distributed manner. Finally, several comparative experiments on sparse signal and image reconstruction demonstrate that our proposed centralized and collective neurodynamic approaches are efficient and effective.

Unrestricted adversarial examples allow the attacker to start attacks without given clean samples, which are quite aggressive and threatening. However, existing works for generating unrestricted adversary examples are quite inefficient and cannot achieve a high success rate. In this article, we explore an end-to-end and effective solution for unrestricted adversary example generation. To stabilize the training process and make our generative model converge to satisfactory results, we design a novel decoupled two-step efficient generative model (EGM), which contains a conditional reference generator and a conditional adversarial transformer. The former is responsible for generating reference samples from noises and source classes. The latter is responsible for converting the reference sample into adversarial examples corresponding to target classes. To improve the success rate, we design a new strategy, augmentation of adversarial labels to produce dynamic target labels and enhance the exploration ability of EGM. Such a strategy can be also applied to existing attacks to improve their attack success rates, which is of independent interest. We conduct extensive experiments to evaluate our proposed model and demonstrate the necessity of decoupling the generation process in EGM. Experimental results show our EGM is much faster and achieves a higher success rate than the state-of-the-art attacks.

Blockchain technology has attracted tremendous interest from both industry and academia. It is typically used to record a public history of transactions (e.g., payment/smart contract data), but storing nonpayment/contract data in transactions has been common. The ability to store data unrelated to payment/contract such as illicit data on blockchain may be abused for malicious purposes. For example, one may use blockchain to store the data related to child pornography and copyright violations, which are publicly visible and immutable. Moreover, an immutable blockchain is not suitable for all blockchain-based applications. So far, numerous redaction mechanisms for the mutable blockchain have been developed. In this paper, we aim at conducting a comprehensive survey that reviews and analyzes the state-of-the-art redaction mechanisms. We start by giving a general presentation of blockchain and summarize the typical methods of inserting data in blockchain. Next, we discuss the challenges of designing the redaction mechanism and propose a list of evaluation criteria. Then, redaction mechanisms of the existing mutable blockchains are systemically reviewed and analyzed based on our evaluation criteria. The analyses include algorithmic overviews, performance limitations, and security vulnerabilities. Finally, the comparisons and analyses provide new insights into these mechanisms. This survey will provide developers and researchers a comprehensive view and facilitate the design of future mutable blockchains.

The Internet of Things (IoT) is gradually stepping out of its infancy into maturity. Its widespread applications cover from tiny wearable devices to large industrial systems. Although many security solutions have been introduced to address data security and privacy problems caused by the unique characteristics of IoT, how to simultaneously achieve data confidentiality, protect the privacy of access control policy, and provide reasonable data source identification has been a challenging problem. Moreover, lacking one of the above properties may result in serious issues (e.g., leakage information and forging identity), and the situation grows steadily worse with the expansion of “things” scale. To address the above issues, we propose a new cryptographic primitive named certificateless matchmaking encryption (CL-ME), which inherits the security properties of certificateless cryptosystem and matchmaking encryption. Meanwhile, we also present two effective concrete constructions with formal security proofs based on the standard hard assumptions. The basic construction is the first instance of CL-ME based on bilinear pairing, and the enhanced construction is a pairing-free lightweight solution. Finally, we implement our proposed schemes using popular cryptography library and compare their performance with existing works. Theoretical analysis and experimental evaluations demonstrate that our proposed schemes are more suitable for IoT environment.

This paper addresses the problem of lossy outsourcing, i.e., clients outsource computation needs to the cloud side through lossy channels, which is very common in practice. We focus on the case that the clients transmit 2D sparse signals to the semi-trusted clouds over packet-loss networks, and the clouds provide sparse robustness decoding service (SRDS) for the users. In order to achieve high level of efficiency and security, we propose to jointly exploit parallel compressive sensing for robust signal encoding and employ multiple cloud servers for SRDS. Specifically, prior to encoding, a signal is encrypted by only altering the indices and amplitudes of its non-zero entries. The encrypted signal is sensed using a Gaussian measurement matrix and the generated compressive measurements are then sent to multi-clouds for SRDS, along with the occurrence of packet loss. Each column in compressive measurements can be regarded as a packet and each description consists of a certain number of packets. Each description together with a small portion of support set is distributed to a cloud. When receiving the request from a user, each cloud performs SRDS using the acquired description, where the reconstructed signal is still in encrypted form so that the signal privacy is well preserved. After receiving the reconstructed signal, the user accomplishes the decryption operation. Experimental results show that the encryption algorithm improves compressibility and reconstruction performance compared with the case of no encryption, and the proposed privacy-assured outsourcing of SRDS is highly robust and efficient.

The visual security index (VSI) is a quantized indicator for objective visual security evaluation of selectively encrypted images. One challenging problem in current research is that the performance of VSIs is highly sensitive to the extracted features and the method of similarity measurement, and it is hard to choose appropriate handcrafted features from encrypted images, as well as to find an effective similarity measurement. In this paper, we make the first attempt to present a novel convolutional neural network-based visual security index (CNNVSI). Our proposed CNNVSI is purely data-driven and trained end-to-end. We propose three specialized designs to make the approach work for encrypted low-quality images without any handcrafted features or prior knowledge about the human vision system (HVS). First, we present a patch labeling algorithm to assign each encrypted patch a visual security score. Second, we design a multiscale attention residual network (MARNet) for feature learning. Last, we propose to fuse the learned features from plain images, encrypted images and their discrepancy images. Extensive and systematic experiments are conducted on five publicly available image databases to analyze the performance of our proposed CNNVSI, and the experimental results and their analysis demonstrate that our proposed CNNVSI significantly outperforms the existing state-of-the-art methods in terms of accuracy and stability.

Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively train a model under the coordination of a central server. The clients' raw data are locally stored, and each client only uploads the trained weight to the server, which can mitigate the privacy risks from the centralized machine learning. However, most of the existing FL models focus on one-time learning without consideration for continuous learning. Continuous learning supports learning from streaming data continuously, so it can adapt to environmental changes and provide better real-time performance. In this article, we present a federated continuous learning scheme based on broad learning (FCL-BL) to support efficient and accurate federated continuous learning (FCL). In FCL-BL, we propose a weighted processing strategy to solve the catastrophic forgetting problem, so FCL-BL can handle continuous learning. Then, we develop a local-independent training solution to support fast and accurate training in FCL-BL. The proposed solution enables us to avoid using a time-consuming synchronous approach while addressing the inaccurate-training issue rooted in the previous asynchronous approach. Moreover, we introduce a batch-asynchronous approach and broad learning (BL) technique to guarantee the high efficiency of FCL-BL. Specifically, the batch-asynchronous approach reduces the number of client-server interaction rounds, and the BL technique supports incremental learning without retraining when learning newly produced data. Finally, theoretical analysis and experimental results further illustrate that FCL-BL is superior to the existing FL schemes in terms of efficiency and accuracy in FCL.

This article investigates the problem of distributed online optimization for a group of units communicating on time-varying unbalanced directed networks. The main target of the set of units is to cooperatively minimize the sum of all locally known convex cost functions (global cost function) while pursuing the privacy of their local cost functions being well masked. To address such optimization problems in a collaborative and distributed fashion, a differentially private-distributed stochastic subgradient-push algorithm, called DP-DSSP, is proposed, which ensures that units interact with in-neighbors and collectively optimize the global cost function. Unlike most of the existing distributed algorithms which do not consider privacy issues, DP-DSSP via differential privacy strategy successfully masks the privacy of participating units, which is more practical in applications involving sensitive messages, such as military affairs or medical treatment. An important feature of DP-DSSP is tackling distributed online optimization problems under the circumstance of time-varying unbalanced directed networks. Theoretical analysis indicates that DP-DSSP can effectively mask differential privacy as well as can achieve sublinear regrets. A compromise between the privacy levels and the accuracy of DP-DSSP is also revealed. Furthermore, DP-DSSP is capable of handling arbitrarily large but uniformly bounded delays in the communication links. Finally, simulation experiments confirm the practicability of DP-DSSP and the findings in this article.

Mobile cloud computing (MCC) as an emerging computing paradigm enables mobile devices to offload their computation tasks to nearby resource-rich cloudlets so as to augment computation capability and reduce energy consumption of mobile devices. However, due to the mobility of mobile devices and the admission of cloudlets, the connection between mobile devices and cloudlets may be unstable, which will affect offloading decision, even cause offloading failure. To address such an issue, in this paper, we propose a robust computation offloading strategy with failure recovery (RoFFR) in an intermittently connected cloudlet system aiming to reduce energy consumption and shorten application completion time. We first provide an optimal cloudlet selection policy when multiple cloudlets are available near mobile devices. Furthermore, we formulate the RoFFR problem as two optimization problems, i.e., local execution cost minimization problem and offloading execution cost minimization problem while satisfying the task-dependency requirement and application completion deadline constraint. By solving both optimization problems, we present a distributed RoFFR algorithm for CPU clock frequency configuration in local execution and transmission power allocation and data rate control in cloudlet execution. Experimental results in a real testbed show that our distributed RoFFR algorithm outperforms several baseline policies and existing offloading schemes in terms of application completion cost and offloading data rate.

In this article, the problem of couple-group consensus for the heterogeneous multiagent systems (MASs) composed of first-order and second-order agents are investigated by hybrid pinning and adaptive control methods, respectively. A novel distributed control protocol of group consensus is proposed via the cooperative-competitive relationship between the agents. Based on algebraic graph theory, matrix analysis and Lyapunov stability theorem, the sufficient conditions and corresponding pinning strategies for the heterogeneous MASs with the weakly connected topology are obtained. In addition, we find that the value of pinning gain should meet a certain condition for guaranteeing the achievement of group consensus. Finally, the validity of our results is verified by some simulated examples.

In this article, we concentrate on dealing with the distributed optimization problem over a directed network, where each unit possesses its own convex cost function and the principal target is to minimize a global cost function (formulated by the average of all local cost functions) while obeying the network connectivity structure. Most of the existing methods, such as push-sum strategy, have eliminated the unbalancedness induced by the directed network via utilizing column-stochastic weights, which may be infeasible if the distributed implementation requires each unit to gain access to (at least) its out-degree information. In contrast, to be suitable for the directed networks with row-stochastic weights, we propose a new directed distributed Nesterov-like gradient tracking algorithm, named as D-DNGT, that incorporates the gradient tracking into the distributed Nesterov method with momentum terms and employs nonuniform step-sizes. D-DNGT extends a number of outstanding consensus algorithms over strongly connected directed networks. The implementation of D-DNGT is straightforward if each unit locally chooses a suitable step-size and privately regulates the weights on information that acquires from in-neighbors. If the largest step-size and the maximum momentum coefficient are positive and small sufficiently, we can prove that D-DNGT converges linearly to the optimal solution provided that the cost functions are smooth and strongly convex. We provide numerical experiments to confirm the findings in this article and contrast D-DNGT with recently proposed distributed optimization approaches.

This article investigates the privacy-preserving average consensus problem for the general continuous-time multiagent network systems (MANSs) with time delay via the event-triggered communication scheme. In order to avoid disclosing the initial states of the network agents and at the same time achieve the agents' consensus for MANSs with time delay, a novel consensus control algorithm based on a new privacy-preserving approach is proposed. The new privacy-preserving approach is that we construct an output mask to make agents' internal states indiscernible by others, which is different from the existing privacy-preserving methods adding random noises to the update law of agents' states. Compared with the existing privacy-preserving methods, our approach makes all agents in MANSs exactly converge to the average value of initial states instead of its mean square value. Based on the proposed algorithm, we carry out the detailed theoretical consensus analysis of the network agents, from which, it is shown that the upper bound of the communication time delay between neighbors' agents can be estimated approximately. Moreover, the Zeno-behavior of event-triggered time sequences for each agent is excluded. Finally, two simulation examples are performed to demonstrate the effectiveness of our theoretical results.

With the technological advances in wireless multimedia domain, these videos made by mobile edge devices dominate network traffics. The video summarization technology enables users to understand the storyline of a video before a client requests the complete video content. Summarizing a video on edge devices and transmitting the summary between them requires a user-oriented and adaptive solution due to the limited capability and the dynamic wireless links of edge devices. Therefore, it is beneficial to improve the user's viewing experience and the bandwidth utilization ratio if we generate and transmit a video summary based on network connections and the user's tolerant latency. Unfortunately, previous summarization approaches are incapable of adjusting the summary size adapted to the varying network bandwidth and the user's attitude towards latency. To timely and flexibly deal with mobile videos, we first formulate the video summarization optimization problem with the elastic number of selected representative segments and the outlier detection within a bounded time budget. Furthermore, we develop an online greedy algorithm called the Elastic Video Summarization Algorithm (EVS) to solve the NP hard problem. We analyze the properties associated with EVS and further design an improved EVS-II to reduce computation complexity. Finally, the experimental results demonstrate that our proposed algorithms outperform other existing researches in fitting network bandwidth and detecting outliers.

The real-time data generated from various smart devices will be released and shared for public to obtain numerous benefits. However, it will lead to individual privacy leakage because of data mining or analysis. Currently, many existing privacy protection models either fail to be directly applied in real-time data release or are unsatisfactory in terms of data utility and privacy protection. Toward this end, based on m-signature and fuzzy processing, an anonymous privacy protection model, named PMF, is proposed in this paper. Specifically, for the proposed model there are five advantages: 1) PMF defines m-signature for making each bucket with at least m different sensitive values instead of generating any counterfeit tuples, which can not only resist h-difference attack but also improve practical value; 2) the buckets satisfying m-signature are variable over time, and this flexibility of m-signature can improve the efficiency of dynamic update; 3) PMF can effectively insert, delete, and modify real-time data for release; 4) PMF applies fuzzy processing to handle the tuples in the candidate list, which strikes a good balance between the utility of released data and privacy protection; and 5) PMF adopts greedy heuristic algorithm to process update operations, which greatly reduces the information loss of released data. Furthermore, PMF is obviously more secure than the existing models in the real-time data release. Finally, the results of the comparison experiments on real-world and synthetic datasets illustrate that PMF is superior to the existing models in terms of data utility and efficiency.

This paper studies a class of distributed convex optimization problems by a set of agents in which each agent only has access to its own local convex objective function and the estimate of each agent is restricted to both coupling linear constraint and individual box constraints. Our focus is to devise a distributed primal-dual gradient algorithm for working out the problem over a sequence of time-varying general directed graphs. The communications among agents are assumed to be uniformly strongly connected. A column-stochastic mixing matrix and a fixed step-size are applied in the algorithm which exactly steers all the agents to asymptotically converge to a global optimal solution. Based on the standard strong convexity and the smoothness assumptions of the objective functions, we show that the distributed algorithm is capable of driving the whole network to geometrically converge to an optimal solution of the convex optimization problem only if the step-size does not exceed some upper bound. We also give an explicit analysis for the convergence rate of the proposed optimization algorithm. Simulations on economic dispatch problems and demand response problems in power systems are performed to illustrate the effectiveness of the proposed optimization algorithm.

Blockchain, as a secured, trusted, and decentralized architecture, is used to create secure and tamper-proof payment schemes, which can serve economies and societies without trusted parties. However, the transparency and traceability of blockchain severely restrict the anonymity of participants in the real world, which will cause participants' privacy leakage. Toward this end, in this paper, an anonymous off-blockchain micropayments scheme (AOM) is proposed for cryptocurrencies in the real world. In AOM, a payee receives micropayments from an “honest-but-curious” intermediary T by solving puzzles which are generated based on the standard RSA assumption. Meanwhile, T also receives micropayments from the payers by payee's solutions and T will randomly select the inputs of the merging transaction Tmer. In order to improve service efficiency of T and resist denial of service attack, one of the outputs of Tmeris paid for T as a service fee. Besides, AOM simultaneously ensures the correctness and fairness of transactions. Finally, from the analyses of property and security, AOM has strong unlinkability, ability for anti-attacks and unforgeability.

In this paper, the event-triggered consensus problem of multiagent systems with input time delay is investigated. First, the normal event-triggered control scheme containing the input time delay is introduced to reduce the number of communication. Then the following results are achieved: 1) the procedure of setting parameters is carefully formulated for the event-triggered control scheme; 2) the precise input time delay margin is calculated for the event-triggered consensus of the multiagent systems; 3) a more general condition of constructing event-triggered functions is derived to exclude the Zeno behavior; 4) the self-triggered control scheme is further applied to avoid the continuous measurement; and 5) the observer-based control scheme is also utilized to tackle the problem of unmeasurable state. Finally, the correctness and the effectiveness of these results are demonstrated by numerical simulations.

As you open this January issue of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), I hope everyone enjoyed a great holiday season and is excited for the new year of 2019. I am very delighted and honored to report several key metrics of IEEE TNNLS to the community.

A Filippov system of Hindmarsh-Rose (HR) neuronal model with threshold policy control is proposed, membrane potential has been taken as the threshold and the corresponding switching function is also established. We first discuss the existence and stability of the equilibria for the two Filippov subsystems based on the 2-D HR model. Subsequently, the sliding dynamics of HR model including the sliding segments, sliding regions, and various equilibria under the Filippov framework are studied. Then, we further consider the equilibria and the sliding bifurcation set of the Filippov system, and find there exist the bistable equilibria and several sliding bifurcation phenomena, such as boundary-node bifurcation, pseudosaddle-node bifurcation, the emergence and disappearance of limit cycles on the sliding line, and so on. Finally, we study the Filippov system of the 3-D HR model, and provide a phase diagram of the system that generates the sliding spiking and the sliding bursting, which lie on the sliding line.

This paper considers solving a class of optimization problems which are modeled as the sum of all agents' convex cost functions and each agent is only accessible to its individual function. Communication between agents in multiagent networks is assumed to be limited: each agent can only interact information with its neighbors by using time-varying communication channels with limited capacities. A technique which overcomes the limitation is to implement a quantization process to the interacted information. The quantized information is first encoded as a binary sequence at the side of each agent before sending. After the binary sequence is received by the neighboring agent, corresponding decoding scheme is utilized to resume the original information with a certain degree of error which is caused by the quantization process. With the availability of each agent's encoding states (associated with its out-channels) and decoding states (associated with its in-channels), we devise a set of distributed optimization algorithms that generate two iterative sequences, one of which converges to the optimal solution and the other of which reaches to the optimal value. We prove that if the parameters satisfy some mild conditions, the quantization errors are bounded and the consensus optimization can be achieved. How to minimize the number of quantization level of each connected communication channel in fixed networks is also explored thoroughly. It is found that, by properly choosing system parameters, one bit information exchange suffices to ensure consensus optimization. Finally, we present two numerical simulation experiments to illustrate the efficacy of the algorithms as well as to validate the theoretical findings.

This paper considers the problem of designing adaptive learning algorithms to seek the Nash equilibrium (NE) of the constrained energy trading game among individually strategic players with incomplete information. In this game, each player uses the learning automaton scheme to generate the action probability distribution based on his/her private information for maximizing his own averaged utility. It is shown that if one of admissible mixed-strategies converges to the NE with probability one, then the averaged utility and trading quantity almost surely converge to their expected ones, respectively. For the given discontinuous pricing function, the utility function has already been proved to be upper semicontinuous and payoff secure which guarantee the existence of the mixed-strategy NE. By the strict diagonal concavity of the regularized Lagrange function, the uniqueness of NE is also guaranteed. Finally, an adaptive learning algorithm is provided to generate the strategy probability distribution for seeking the mixed-strategy NE.

This paper is concerned with pinning consensus control problem for multi-agent networks with general connected topology which neither needs to be strongly connected nor needs to contain a directed spanning tree. By adopting adaptive control laws, some promising criteria are proposed analytically, which can guarantee the networks to achieve consensus asymptotically. Meanwhile, a novel and effective pinning scheme is addressed as well. In addition, it is interesting to find out that the rule does not always hold, that is the more nodes are pinned, the faster the network converges. Finally, the validity and correctness of our theoretical findings are verified by several numerical simulated experiments.

In this letter, a novel kernel adaptive filtering algorithm, namely the kernel least mean square with single feedback (SF-KLMS) algorithm, is proposed. In SF-KLMS, only a single delayed output is used to update the weights in a recurrent fashion. The use of past information accelerates the convergence rate significantly. Compared with the kernel adaptive filter using multiple feedback, SF-KLMS has a more compact and efficient structure. Simulations in the context of time-series prediction and nonlinear regression show that SF-KLMS outperforms not only the kernel adaptive filter with multiple feedback but also the kernel adaptive filter without feedback in terms of convergence rate and mean square error.

In this paper, we consider the second-order globally nonlinear consensus in a multiagent network with general directed topology and random interconnection failure by characterizing the behavior of stochastic dynamical system with the corresponding time-averaged system. A criterion for the second-order consensus is derived by constructing a Lyapunov function for the time-averaged network. By associating the solution of random switching nonlinear system with the constructed Lyapunov function, a sufficient condition for second-order globally nonlinear consensus in a multiagent network with random directed interconnections is also established. It is required that the second-order consensus can be achieved in the time-averaged network and the Lyapunov function decreases along the solution of the random switching nonlinear system at an infinite subsequence of the switching moments. A numerical example is presented to justify the correctness of the theoretical results.

In this brief, the asymptotic stability properties of a neutral delay neuron system are studied mainly in a critical case when the exponential stability is not possible. If a critical value of the coefficient in the neutral delay neuron system is considered, then the difficulty for our investigation is caused by the fact that the spectrum of the linear operator is asymptotically approximated to the imaginary axis. It is obvious that, in such a case, the equation is not exponentially stable, and one needs more subtle methods in order to characterize this type of asymptotic stability. Hence, first, the local asymptotic stability for the neutral delay neuron system is studied, and the main tools involved are the asymptotic expansions of characteristic roots, Laplace transforms, and function series, and a complete analysis of the stability diagram is also presented. Then, based on the energy method, the globally asymptotic stability results for the neutral delay neuron system are derived.

Cloud computing paradigm provides an alternative and economical service for resource-constrained clients to perform large-scale data computation. Since large matrix determinant computation (DC) is ubiquitous in the fields of science and engineering, a first step is taken in this paper to design a protocol that enables clients to securely, verifiably, and efficiently outsource DC to a malicious cloud. The main idea to protect the privacy is employing some transformations on the original matrix to get an encrypted matrix which is sent to the cloud; and then transforming the result returned from the cloud to get the correct determinant of the original matrix. Afterwards, a randomized Monte Carlo verification algorithm with one-sided error is introduced, whose superiority in designing inexpensive result verification algorithm for secure outsourcing is well demonstrated. In addition, it is analytically shown that the proposed protocol simultaneously fulfills the goals of correctness, security, robust cheating resistance, and high-efficiency. Extensive theoretical analysis and experimental evaluation also show its high-efficiency and immediate practicability. It is hoped that the proposed protocol can shed light in designing other novel secure outsourcing protocols, and inspire powerful companies and working groups to finish the programming of the demanded all-inclusive scientific computations outsourcing software system. It is believed that such software system can be profitable by means of providing large-scale scientific computation services for so many potential clients.

In this technical correspondence, we consider a distributed cooperative optimization problem encountered in a computational multiagent network with delay, where each agent has local access to its convex cost function, and jointly minimizes the cost function over the whole network. To solve this problem, we develop an algorithm that is based on dual averaging updates and delayed subgradient information, and analyze its convergence properties for a diminishing step-size by utilizing Bregman-distance functions. Moreover, we provide sharp bounds on the convergence rates as a function of the network size and topology embodied in the inverse spectral gap. Finally, we present a numerical example to evaluate our algorithm and compare its performance with several similar algorithms.

In this paper, the asymptotic stability properties of neutral-type neuron system are studied mainly in the critical case when the exponential stability is not possible. In the case of a critical value of the coefficient in neutral-type neuron system, the difficulty for our investigation is the fact that the spectrum of the linear operator is asymptotically approximated to the imaginary axis. Hence, based on the energy method, the asymptotic stability results for neutral-type neuron system are derived, and a complete analysis of the stability diagram is presented.

This paper formulates and studies a more general model of coupled switched neural networks with impulsive time window. The main feature of impulsive time window is that impulses can exist the stochastic instants of the whole switching interval not the switching instants and a pre-specified instants. Moreover, the impulsive numbers of every subsystems is not the same. Using switching Lyapunov functions and a generalized Halany inequality, some general criteria which characterize the impulses and switching effects in aggregated form, for asymptotically synchronization and exponential synchronization of this general model are established.

The future behavior of a dynamical system is determined by its initial state or initial function. Nontrivial neuron system involving adaptive learning corresponds to the memorization of initial information. In this paper, exponential estimates and sufficient conditions for the exponential stability of a single neuron system of neutral-type are studied. Of particular importance is the fact that exponential convergence guarantees that this system is capable of memorizing initial functions. Furthermore, this system is also capable of conveying much more information with respect to the initial functions memorized by neuron system with time delay. The proofs follow some new results on nonhomogeneous difference equations evolving in continuous-time combined with the Lyapunov-Krasovskii functional and the descriptor system approach. The exponential stability conditions are expressed in terms of a linear matrix inequality, which lead to less restrictive and less conservative exponential estimates.

Body Area Networks (BANs) are expected to play a major role in patient health monitoring in the near future. Providing an efficient key agreement with the prosperities of plug-n-play and transparency to support secure inter-sensor communications is critical especially during the stages of network initialization and reconfiguration. In this paper, we present a novel key agreement scheme termed Ordered-Physiological-Feature-based Key Agreement (OPFKA), which allows two sensors belonging to the same BAN to agree on a symmetric cryptographic key generated from the overlapping physiological signal features, thus avoiding the pre-distribution of keying materials among the sensors embedded in the same human body. The secret features computed from the same physiological signal at different parts of the body by different sensors exhibit some overlap but they are not completely identical. To overcome this challenge, we detail a computationally efficient protocol to securely transfer the secret features of one sensor to another such that two sensors can easily identify the overlapping ones. This protocol possesses many nice features such as the resistance against brute force attacks. Experimental results indicate that OPFKA is secure, efficient, and feasible. Compared with the state-of-the-art PSKA protocol, OPFKA achieves a higher level of security at a lower computational overhead.

Traditional topology control designs of wireless sensor networks (WSNs) usually assume reliable links between two sensors if their distance is less than a threshold. In real environments, however, relaxing the reliable threshold to utilize lossy links is beneficial for saving node transmission power so that the network lifecycle can be prolonged. Since transmission over lossy links is unreliable, data retransmission is required. But retransmission results in extra energy consumption and remarkable communication delay. In view of this, we define an enhanced version of restricted shortest path (RSP) problem, called end-to-end delay-constrained topology control problem in lossy WSNs (Lossy-RSP), which takes into account retransmission costs in terms of energy and time. The difficulty in the Lossy-RSP problem is to deal with parallel edges collision phenomenon, i.e., different power levels between two adjacent nodes are suited for constructing constrained shortest paths between different sources and destinations. The Lossy-RSP problem is NP-complete in strong sense due to parallel edges collision phenomenon. We present two independent heuristic algorithms to defuse this phenomenon while retaining energy-efficient links. Simulation results show that our algorithms can reduce the energy consumption significantly even under rather strict delay constraint.

Body Area Networks (BANs) are expected to play a major role in the field of patient-health monitoring in the near future. While it is vital to support secure BAN access to address the obvious safety and privacy concerns, it is equally important to maintain the elasticity of such security measures. For example, elasticity is required to ensure that first-aid personnel have access to critical information stored in a BAN in emergent situations. The inherent tradeoff between security and elasticity calls for the design of novel security mechanisms for BANs. In this paper, we develop the Fuzzy Attribute-Based Signcryption (FABSC), a novel security mechanism that makes a proper tradeoff between security and elasticity. FABSC leverages fuzzy Attribute-based encryption to enable data encryption, access control, and digital signature for a patient's medical information in a BAN. It combines digital signatures and encryption, and provides confidentiality, authenticity, unforgeability, and collusion resistance. We theoretically prove that FABSC is efficient and feasible. We also analyze its security level in practical BANs.

Cloud computing enables resource-constrained clients to economically outsource their huge computation workloads to a cloud server with massive computational power. This promising computing paradigm inevitably brings in new security concerns and challenges, such as input/output privacy and result verifiability. Since matrix inversion computation (MIC) is a quite common scientific and engineering computational task, we are motivated to design a protocol to enable secure, robust cheating resistant, and efficient outsourcing of MIC to a malicious cloud in this paper. The main idea to protect the privacy is employing some transformations on the original matrix to get a encrypted matrix which is sent to the cloud; and then transforming the result returned from the cloud to get the correct inversion of the original matrix. Next, a randomized Monte Carlo verification algorithm with one-sided error is employed to successfully handle result verification. In this paper, the superiority of this novel technique in designing inexpensive result verification algorithm for secure outsourcing is well demonstrated. We analytically show that the proposed protocol simultaneously fulfills the goals of correctness, security, robust cheating resistance, and high-efficiency. Extensive theoretical analysis and experimental evaluation also show its high-efficiency and immediate practicability.

The Arnold cat map is employed in various applications where chaos is utilized, especially chaos-based cryptography and watermarking. In this paper, we study the problem of period distribution of the generalized discrete Arnold cat map over the Galois ring \BBZ2e. Full knowledge of the period distribution is obtained analytically by adopting the Hensel lift approach. Our results have impact on both chaos theory and its applications as they not only provide design strategy in applications where special periods are required, but also help to identify unstable periodic orbits of the original chaotic cat map. The method in our paper also shows some ideas how to handle problems over the Galois ring \BBZ2e.

This paper is mainly concerned with the analysis of the second-order locally dynamical consensus of multiagent systems with nonlinear dynamics in the directed networks with arbitrarily fast switching topologies. In our designed framework, the time-varying network topology is constant in each time interval and then randomly jumps to another topology when certain occasional events occur at some random moments. In addition, we further assume that the dwell time of each topology is unknown in advance and the corresponding adjacency weighted matrix is not necessarily nonnegative due to the probable existence of deteriorated communication channels in the underlying interaction network. By the orthogonal decomposition method, the state vector of the resultant error dynamical system can be further decomposed as two transversal components, one of which evolves along the consensus manifold and the other evolves transversally with the consensus manifold. Then, by introducing the generalized matrix measure and by applying the tools of contraction and circle analysis, the second-order locally dynamical consensus of multiagent systems with arbitrarily fast switching directed topologies is theoretically investigated in detail, and some easily verified sufficient conditions are also presented. It is shown that, under sufficiently large coupling strengths, the random switchings can be effectively tolerated and the consensus can be achieved for all agents in the network. Finally, numerical simulation examples are also provided to demonstrate the feasibility and effectiveness of the obtained theoretical results.

In this paper, we analyze the period distribution of the generalized discrete cat map over the Galois ring where is a prime. The sequences generated by this map are modeled as 2-dimensional LFSR sequences. Employing the generation function and the Hensel lifting approaches, full knowledge of the detail period distribution is obtained analytically. Our results not only characterize the period distribution of the cat map, which gives insights to various applications, but also demonstrate some approaches to deal with the period of a polynomial in the Galois ring.

This brief studies the global exponential stability of the equilibrium point of discrete-time delayed Hopfield neural networks (DHNNs) with impulse effects by using difference inequalities. We shall consider the stabilizing effects of impulses when the corresponding impulse-free DHNN is even not asymptotically stable. The obtained results characterize the aggregated effects of impulses and deviation of the impulse-free DHNN from its equilibrium point on the exponential stability of the whole system. It is shown that, because of effects of impulses, the impulsive discrete-time DHNN may be exponentially stable even if the evolution of impulse-free component deviates from its equilibrium point exponentially.

Clustering is an important technique in data mining. In this paper, we introduce a new clustering algorithm. This algorithm, based on granular computing, constructs a hypergraph (simplicial complex) by the hypergraph bisection algorithm. It will discover the similarities and associations among documents. In some experiments on Web data, the proposed algorithm is used; the results are quite satisfactory. © 2009 Wiley Periodicals, Inc.

In this paper, the period distribution of sequences generated by Chebyshev polynomials over the finite field ZNis analyzed. It is found that the distribution is unsatisfactory if N (the modulus) is not chosen properly. Based on this finding, we present an attack on the public-key algorithm based on Chebyshev polynomials over ZN. Then, we modify the original algorithm to make it suitable for practical purpose. Its security under some existing models is also discussed in detail.

Using Chaotic characteristics of dynamic system is a promising direction to design cryptosystems that play a pivotal role in a very important engineering application of cognitive informatics, i.e., information assurance and security. However, encryption algorithms based on the lowdimensional chaotic maps face a potential risk of the keystream being reconstructed via return map technique or neural network method. In this paper, we propose a new digital image encryption algorithm that employs a hyper-chaotic cellular neural network. To substantiate its security characteristics, we conduct the following security analyses of the proposed algorithm: key space analysis, sensitivity analysis, information entropy analysis and correlation coefficients analysis of adjacent pixels. The results demonstrate that the proposed encryption algorithm has desirable security properties and can be deployed as a cornerstone in a sound security cryptosystem. The comparison of the proposed algorithm with five other chaos-based image encryption algorithms indicates that our algorithm has a better security performance.

In this paper, an improved particle swarm optimization algorithm with momentum (mPSO) is proposed based on inspiration from the back propagation (BP) learning algorithm with momentum in neural networks. The momentum acts as a lowpass filter to relieve excessive oscillation and also extends the PSO velocity updating equation to a second-order difference equation. Experimental results are shown to verify its superiority both in robustness and efficiency.

This paper presents a novel approach to stability analysis of a fuzzy large-scale system in which the system is composed of a number of Takagi-Sugeno (T-S) fuzzy subsystems with interconnections. The stability analysis is based on Lyapunov functions that are continuous and piecewise quadratic. It is shown that the stability of the fuzzy large-scale systems can be established if a piecewise Lyapunov function can be constructed, and, moreover, the function can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible. It is also demonstrated via a numerical example that the stability result based on the piecewise quadratic Lyapunov functions is less conservative than that based on the common quadratic Lyapunov functions. The H infinity controllers can also be designed by solving a set of LMIs based on these powerful piecewise quadratic Lyapunov functions.

In this paper, the conventional bidirectional associative memory (BAM) neural network with signal transmission delay is intervalized in order to study the bounded effect of deviations in network parameters and external perturbations. The resultant model is referred to as a novel interval dynamic BAM (IDBAM) model. By combining a number of different Lyapunov functionals with the Razumikhin technique, some sufficient conditions for the existence of unique equilibrium and robust stability are derived. These results are fairly general and can be verified easily. To go further, we extend our investigation to the time-varying delay case. Some robust stability criteria for BAM with perturbations of time-varying delays are derived. Besides, our approach for the analysis allows us to consider several different types of activation functions, including piecewise linear sigmoids with bounded activations as well as the usual C1-smooth sigmoids. We believe that the results obtained have leading significance in the design and application of BAM neural networks.

In this paper, a new sufficient condition is given for the global asymptotic stability and global exponential output stability of a unique equilibrium points of delayed cellular neural networks (DCNNs) by using Lyapunov method. This condition imposes constraints on the feedback matrices and delayed feedback matrices of DCNNs and is independent of the delay. The obtained results extend and improve upon those in the earlier literature, and this condition is also less restrictive than those given in the earlier references. Two examples compared with the previous results in the literatures are presented and a simulation result is also given.

In this paper, we investigate the problem of global and robust stability of a class of interval Hopfield neural networks that have time-varying delays. Some criteria for the global and robust stability of such networks are derived, by means of constructing suitable Lyapunov functionals for the networks. As a by-product, for the conventional Hopfield neural networks with time-varying delays, we also obtain some new criteria for their global and asymptotic stability.

In this paper, the asymptotic stability of a two-neuron system with different time delays has been investigated. Some criteria for determining the global asymptotically stability of equilibrium are derived from the theory of monotonic dynamical system and the approach of Lyapunov functional. For local asymptotic stability, some elegant criteria are also obtained by the Nyquist criteria. We find that one of them depends on the length of delays while the other ones do not. In the latter case, the delays are sometimes called harmless delays. The results obtained have leading significance in the study of neural networks composed of a large number of neurons with different time delays.

A continuously delayed neural network with strong kernel is investigated. We found that a switch from stability to instability may occur for certain range of system parameters and must then be followed by a switch back to stability. We also investigate bifurcation phenomena of this model. Using the mean time delay as a bifurcation parameter, we prove that Hopf bifurcation occurs, i.e., a family of periodic solutions bifurcates from the equilibrium when the bifurcation parameter passes through a critical value. Stability criteria for the bifurcating periodic solutions are obtained. Some computer simulations illustrate correctness of the results.

The conventional Hopfield neural network with time delay is intervalized to consider the bounded effect of deviation of network parameters and perturbations yielding a novel interval dynamic Hopfield neural network (IDHNN) model. A sufficient condition related to the existence of unique equilibrium point and its robust stability is derived.