Time series forecasting faces a fundamental challenge: the uneven distribution of predictive importance in time series data, where some specific time points and feature combinations carry disproportionately predictive power. As a result, uniform processing methods that treat all data alike inevitably fall short of optimal performance. To address this problem, we propose FeTS, a feature-aware framework that comprehensively learns temporal features through two key components: (i) Adaptive Feature Extraction (AdaFE), which dynamically discovers the most important features within each temporal patch and extracts them on the fly, yielding sharper and more focused local representations; and (ii) Dual-Scale Feed-Forward Network (DSFFN), which strategically integrates fine-grained local features with global long-term dependencies to achieve richer dual-scale representation learning. Extensive experiments on eight benchmark datasets demonstrate that FeTS achieves state-of-the-art performance in time series forecasting tasks, offering a novel solution to the challenge of uneven predictive importance in forecasting.

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

The rapid advancement and wide application of the Internet of Things technology (IoT) have brought unprecedented convenience to people’s production and life. A great number of devices are connected to the IoT network to provide various services for people, which also makes the IoT more vulnerable to various cyber-attacks. This paper designs a novel IoT web attack detection architecture, which combines the powerful knowledge expression ability and high interpretability of symbolic artificial intelligence (AI) with the adaptive learning ability of connectionist AI to form a closed loop of knowledge embedding and extraction, effectively improve the detection ability of web attacks. The architecture solves the “black box” feature of deep learning models and can obtain knowledge from the trained detection model and add it to the training process of the new model to improve detection capabilities. It also uses the advantages of blockchain technology to realize intelligent sharing between different detection systems, solve the problem of difficult detection model updates and training data acquisition “bottlenecks”. To better detect web attacks, we propose a semi-supervised learning method based on an interpretable convolutional neural network (CNN) to reduce misjudgments during self-training and improve detection accuracy. Additionally, we propose a new feature method to extract the features of web logs in IoT devices, which can help the system to detect web attacks in IoT more quickly and accurately. Simulation results on two different datasets show that the proposed architecture and method can effectively detect web attacks in IoT and reduce the false positive rate.

The advancement of Internet of Things (IoT) technology has significantly transformed the dynamic between humans and devices, as well as device-to-device interactions. This paradigm shift has led to profound changes in human lifestyles and production processes. Through the interconnectedness of numerous sensors and controllers via networks, the IoT facilitates the seamless integration of humans with diverse devices, leading to substantial economic advantages. Nevertheless, the burgeoning IoT industry and the rapid proliferation of various IoT devices have also introduced a multitude of security vulnerabilities. Cyber attackers frequently exploit cyber attacks to compromise IoT devices, jeopardizing user privacy and property security, thereby posing a grave menace to the overall security of the IoT ecosystem. In this paper, we propose a novel IoT Web attack detection system based on a joint embedded prediction architecture (JEPA), which effectively alleviates the security issues faced by IoT. It can obtain high-level semantic features in IoT traffic data through non-generative self-supervised learning. These features can more effectively distinguish normal data from attack data and help improve the overall detection performance of the system. Moreover, we propose a feature interaction module based on a dual-branch network, which effectively fuses low-level features and high-level features, and comprehensively aggregates global features and local features. Simulation results on multiple datasets show that our proposed system has better detection performance and robustness.

The super data transmission capability and connectivity of wireless technologies have promoted the arrival of the Internet of Things (IoT) era. However, the distinct characteristics of IoT devices make them vulnerable to malicious attacks such as hackers and viruses. This paper designs a novel IoT intrusion detection architecture that combines knowledge extraction and sharing, which can extract human understandable knowledge from the trained deep learning model and apply it to the training process of the detection model. The obtained knowledge can also be shared with other detection systems based on the blockchain, which will effectively improve the intrusion detection capabilities of the IoT and realize collective learning. In addition, we propose a CNN-based semi-supervised learning method under the constraints of rules, which can effectively alleviate the catas-trophic interference generated during the self-training process and improve detection accuracy. Simulation results confirm the effectiveness of the proposed architecture and method.

Cross-view image synthesis aims to synthesize a ground-view image covering the same geographic region for a given single aerial-view image (or vice versa). Existing approaches typically tackle this challenging task by relaxing the single-image constraint and using a ground-truth semantic map as additional input to aid synthesis. However, this is nearly infeasible in practice. In this paper, we investigate how to generate a detail-enriched and structurally accurate ground-level image from only a single aerial-level input image, in which there are no other prior knowledge except for the input image. Towards this goal, we propose a novel Progressive Parallel Generative Adversarial Network (PPGAN) that starts from generating low-resolution outputs and progressively produces ground images at higher resolutions as the network propagates forward. In this manner, our PPGAN decomposes the task into several manageable sub-tasks, which helps to generate detail-enriched and structurally accurate ground images. During progressive generation, the PPGAN employs a parallel generation paradigm that enables the generator to produce multi-resolution images in parallel, thereby avoiding excessive time cost on training. Furthermore, for effective information propagation across multi-resolution images, a feature fusion module (FFM) is devised to mitigate the domain gap between cross-level image features, which enables a balance of detail and structural information synthesis. Additionally, the proposed Channel-Space Attention Selection Module (CSASM) learns the mapping relationship between cross-view images in a larger scale space to enhance the quality of the output image. Quantitative and qualitative experiments demonstrate that, our method requires only one input image without the aid of additional inputs, but is capable of synthesizing detail-enriched and structurally accurate ground images and outperforms the existing state-of-the-art methods on two famous benchmarks.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. Meanwhile, UAV’s ability of autonomous navigation and obstacle avoidance becomes more and more critical. This paper focuses on filling up the gap between deep reinforcement learning (DRL) theory and practical application by involving attention mechanism and hierarchical mechanism to solve some severe problems encountered in the practical application of DRL. More specifically, in order to improve the robustness of DRL, we use averaged estimation function instead of the normal value estimation function. Then, we design a recurrent network and a temporal attention mechanism to improve the performance of the algorithm. Third, we propose a hierarchical framework to improve its performance on long-term tasks. Some realistic simulation environments, as well as the real-world, are used to evaluate the proposed UAV autonomous navigation method. The results demonstrate that our DRL-based navigation method performs well in different environments and outperforms the original DrQ algorithm.

Machine learning (ML) algorithms are essential components in autonomous driving. In most existing connected and autonomous vehicles (CAVs), a large amount of driving data collected from multiple vehicles are sent to a central server for unified training. However, data privacy and security have become crucial during the data-sharing process. Federated learning (FL) for data security has arisen nowadays, and it can improve the data privacy of distribute machine learning. However, the malicious attackers can still be able to attack the training process. Due to the complete reliance on the central server, FL is very fragile. To address the above problem, we propose Bift: 1) a fully decentralized ML system combined with FL and 2) blockchain to provide a privacy-preserving ML process for CAVs. Bift enables distributed CAVs to train ML models locally using their own driving data and then to upload the local models to get a better global model. More importantly, Bift provides a consensus algorithm named Proof of Federated Learning to resist possible adversaries. We evaluate the performance of Bift and demonstrate that Bift is scalable and robust, and can defend against malicious attacks.

One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. However, the mask representation might lead to expensive post-processing, while the point sequence one may have limited capability to model texts with highly-curved shapes. To tackle these problems, we model text instances in the Fourier domain and propose one novel Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours as compact signatures. We further construct FCENet with a backbone, feature pyramid networks (FP-N) and a simple post-processing with the Inverse Fourier Transformation (IFT) and Non-Maximum Suppression (N-MS). Different from previous methods, FCENet first pre-dicts compact Fourier signatures of text instances, and then reconstructs text contours via IFT and NMS during test. Extensive experiments demonstrate that FCE is accurate and robust to fit contours of scene texts even with highly-curved shapes, and also validate the effectiveness and the good generalization of FCENet for arbitrary-shaped text detection. Furthermore, experimental results show that our FCENet is superior to the state-of-the-art (SOTA) meth-ods on CTW1500 and Total-Text, especially on challenging highly-curved text subset.

We present MMOCR---an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. MMOCR implements 14 state-of-the-art algorithms, which is significantly more than all the existing open-source OCR projects we are aware of to date. To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. MMOCR is publicly released at https://github.com/open-mmlab/mmocr.

In recent years, with the rapid development of the Internet of Things (IoT), various applications based on IoT have become more and more popular in industrial and living sectors. However, the hypertext transfer protocol (HTTP) as a popular application protocol used in various IoT applications faces a variety of security vulnerabilities. This article proposes a novel HTTP anomaly detection framework based on edge intelligence (EI) for IoT. In this framework, both clustering and classification methods are used to quickly and accurately detect anomalies in the HTTP traffic for IoT. Unlike the existing works relying on a centralized server to perform anomaly detection, with the recent advances in EI, the proposed framework distributes the entire detection process to different nodes. Moreover, a data processing method is proposed to divide the detection fields of HTTP data, which can eliminate redundant data and extract features from the fields of an HTTP header. Simulation results show that the proposed framework can significantly improve the speed and accuracy of HTTP anomaly detection, especially for unknown anomalies.

Distributed deep learning deployed in an edge computing environment is a promising approach for extracting accurate information from raw sensor data from Internet of Things (IoT). But the distributed training suffers from heavy communication overheads between a master node and multiple compute nodes due to frequent transmission of gradients, which limits the training efficiency of the distributed deep learning. In this article, we propose a novel algorithm named ProbComp-LPAC (ProbComp: probability compression and LPAC: layer parameters adaptive compression), which can reduce the communication overhead and improve the training efficiency of the distributed deep learning. ProbComp-LPAC adopts a probability equation to select the gradients and uses different compression rates in different layers of deep neural networks. Comparing with other methods, such as adaptive compression (AdaComp) and lazily aggregated quantized compression (LAQ), the performance of ProbComp-LPAC is not only faster in the training speed but also higher in the accuracy of the test.

Verifiable pattern matching enables users to obtain authenticated query results over outsourced data on an untrusted remote server. It is a fundamental problem in many security-critical big data applications, including big database search, human genome data search, text search, etc., especially when these applications are outsourced to third-party clouds. However, the state-of-the-art schemes do not yet support efficient data updates. In this work, we propose the first dynamic verifiable pattern matching scheme to support efficient data updates. The proposed scheme is built on two ideas: one is to embed unique randomness to decouple the character and its index in the outsourced data, enabling efficient data updates; the other is to reduce the verifiable pattern matching problem to a discrete set membership testing problem, which relies on the decoupling introduced in the first idea. Based on these two ideas, the proposed scheme first employs the suffix array index structure to search pattern matching queries. The scheme then authenticates the outsourced text using a newly designed authenticated data structure based on the RSA accumulator, which guarantees the verifiability of pattern matching query results. Data update is naturally supported using the RSA accumulator working on discrete sets. Based on the proposed design, we have prototyped a proof-of-concept for the proposed scheme and have conducted an extensive experimental evaluation. In addition to supporting efficient data update, our experimental results show that the proposed scheme incurs reduced verification cost in comparison with the baseline state-of-the-art scheme.

Traditional reproduction operators in many-objective evolutionary algorithms (MaOEAs) seem to not be so effective to tackle many-objective optimization problems (MaOPs). This is mainly because the population size cannot be set to an arbitrarily large value if the computational efficiency is of concern. In such a case, the distance between the parents becomes remarkably large and, consequently, it is not easy to reproduce a superior offspring in high-dimensional objective space. To alleviate this problem, an elite gene-guided (EGG) reproduction operator is proposed to tackle MaOPs in this article. In this operator, an elite gene pool is built by collecting the knee points from the current population. Then, the offspring is produced by exchanging the genes with this elite gene pool under an exchange rate, aiming to reserve more promising genes into the next generation. In order to provide new genes for the population, other genes will be disturbed under a disturbance rate. The settings and functional analysis of the exchange rate and disturbance rate are studied using several experiments. The proposed EGG operator is easy to implement and can be embedded to any MaOEA. As examples, we show the embedding of the proposed EGG operator into four competitive MaOEAs, that is, MOEA/D, NSGA-III, θ -DEA, and SPEA2-SDE provide some advantages over simulated binary crossover, differential evolution, and an evolutionary path-based reproduction operator on solving a number of benchmark problems with 3 to 15 objectives.

Vehicle ad hoc networks (VANETs) are considered to be the next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Due to the very fast move and high dynamic in VANETs, it is important to quickly ascertain the reliability of information. Although intrusion detection system (IDS) has been proposed as a reliable approach to protect VANETs against attacks, its overhead is serious, which spends too much time on detection, especially when the number of vehicles increases. Thus, in this paper, we propose a novel filter model based on a hidden generalized mixture transition distribution model (HgMTD) in VANETs, called FM-HgMTD, which can quickly filter the messages from neighboring vehicles so as to reduce the overhead and detection time. It adopts a well-known multi-objective optimization (NSGA-II) algorithm combined with an expectation-maximization (EM) algorithm to forecast the future states of neighboring vehicles and then to filter out malicious messages, by monitoring the change of the state pattern of each neighboring vehicle. In addition, a timeliness method is used to maintain the accuracy of the forecast. The experiments show that IDS with the proposed FM-HgMTD has better performance than other available IDSs in terms of detection rate, detection time, and overhead.

This paper proposes a novel multiobjective particle swarm optimization algorithm with dynamic resource allocation, showing promising performance especially for tackling some complicated multiobjective optimization problems. With the decomposition approach, each particle is assigned to optimize one subproblem with a novel velocity update strategy to speed up the convergence. Moreover, a dynamic resource allocation strategy is designed based on the relative improvement of subproblems, which can reasonably allocate computational resource to the particles that are able to search superior solutions. By this way, the proposed algorithm not only has strong exploratory capability, but also can converge quickly to the true Pareto-optimal front. The experimental results fully demonstrate the superiority of our proposed algorithm over four state-of-the-art multiobjective optimization algorithms, when tackling thirty-five test problems.

For real-world optimization problems, a uniformly and widely distributed Pareto optimal set (PS) in the decision space can provide more choices for decision makers. However, most of multi-objective evolutionary algorithms (MOEAs) only consider convergence and diversity in the objective space, which rarely pay attention to diversity in the decision space. Especially for multimodal multi-objective optimization problems (MMOPs), there may exist multiple distinct PSs corresponding to the same Pareto front (PF). Thus, we propose a novel multimodal multi-objective evolutionary algorithm using a density-based one-by-one update strategy in this paper, which considers diversity in both the objective and decision spaces. In the proposed algorithm, once an offspring is generated during evolution, the most crowded subregion with the largest niche count in the objective space has to be identified again, helpful to maintain diversity in the objective space. Furthermore, the harmonic average distance approach is used to estimate the global density of solutions in the decision space, trying to maintain the population's diversity in the decision space. Our proposed algorithm is compared with several state-of-the-art algorithms on MMOPs. The experimental results demonstrate that our algorithm is capable of preserving promising solutions with even distribution in both of decision space and objective space and also shows the superiority on solving the adopted MMOPs.

This paper suggests a novel clustering-based evolutionary algorithm for many-objective optimization problems. Its main idea is to classify the population into a number of clusters, which is expected to solve the difficulty of balancing convergence and diversity in high-dimensional objective space. The individuals showing high similarities on the vector angles are gathered into the same cluster, such that the population’s distribution can be well portrayed by the clusters. To efficiently find these clusters, partitional clustering is first used to classify the union population into${m}$main clusters based on the${m}$axis vectors (${m}$is the number of objectives), and then hierarchical clustering is further run on these${m}$main clusters to get${N}$final clusters (${N}$is the population size and${N>m}$). At last, in environmental selection, one individual from each of${N}$clusters closest to the axis vectors is selected to maintain diversity, while one individual from each of the other clusters is preferred by a simple convergence indicator to ensure convergence. When tackling some well-known test problems with 5–15 objectives, extensive experiments validate the superiority of our algorithm over six competitive many-objective EAs, especially on problems with incomplete and irregular Pareto-optimal fronts.

This paper proposes an effective ensemble framework (EF) for tackling multiobjective optimization problems, by combining the advantages of various evolutionary operators and selection criteria that are run on multiple populations. A simple ensemble algorithm is realized as a prototype to demonstrate our proposed framework. Two mechanisms, namely competition and cooperation, are employed to drive the running of the ensembles. Competition is designed by adaptively running different evolutionary operators on multiple populations. The operator that better fits the problem’s characteristics will receive more computational resources, being rewarded by a decomposition-based credit assignment strategy. Cooperation is achieved by a cooperative selection of the offspring generated by different populations. In this way, the promising offspring from one population have chances to migrate into the other populations to enhance their convergence or diversity. Moreover, the population update information is further exploited to build an evolutionary potentiality model, which is used to guide the evolutionary process. Our experimental results show the superior performance of our proposed ensemble algorithms in solving most cases of a set of 31 test problems, which corroborates the advantages of our EF.

Recently, more and more users and enterprises have entrusted data storage and platform construction to proxy cloud service provider (PCSP) through cloud technology. Under this background, the attribute-based encryption (ABE) mechanism is an alternative to fill the drawbacks of the traditional encryption through flexible fine-grained access policy and collusion prevention. However, there exist some security issues when the access policy and file need to be updated in practical applications. And the ABE has the problems of excessive computation and storage costs. In this article, an efficient ciphertext-policy ABE scheme with policy update and file update is proposed in cloud computing. The ciphertext components generated by first encryption can be shared when the policy update and file update happens. It reduces the storage and communication costs of the client, and the computational cost of the PCSP. Moreover, the proposed scheme is proved to be secure under the assumption of decision q-parallel bilinear Diffie–Hellman exponent (BDHE). Finally, experimental simulation shows that the proposed scheme is highly efficient in terms of policy update and file update.

The multiobjective evolutionary algorithm (MOEA) based on decomposition transforms a multiobjective optimization problem into a set of aggregated subproblems and then optimizes them collaboratively. Since these subproblems usually have different degrees of difficulty, resource allocation (RA) strategies have been reported to enhance performance, attempting to dynamically assign proper amounts of computational resources for the solution of each of these subproblems. However, existing schemes for decomposition-based MOEAs fully rely on the relative improvement of the aggregated functions to do this. This paper proposes a diversity-enhanced RA strategy for this kind of MOEA, depending on both relative improvement on aggregated function value and solution density around each subproblem to assign computational resources. Thus, one subproblem surrounded with fewer solutions in its neighboring area and more relative improvement on the aggregated function value will be allocated a higher probability for evolution. Our experimental results show the advantages of our proposed strategy over two popular RA strategies available for decomposition-based MOEAs, on tackling a set of complicated benchmark problems.

Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform poorly when tackling many-objective optimization problems (MaOPs). This is mainly because the loss of selection pressure that occurs when updating the swarm. The number of nondominated individuals is substantially increased and the diversity maintenance mechanisms in MOPSOs always guide the particles to explore sparse regions of the search space. This behavior results in the final solutions being distributed loosely in objective space, but far away from the true Pareto-optimal front. To avoid the above scenario, this paper presents a balanceable fitness estimation method and a novel velocity update equation, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs. Moreover, an evolutionary search is further run on the external archive in order to provide another search pattern for evolution. The DTLZ and WFG test suites with 4-10 objectives are used to assess the performance of NMPSO. Our experiments indicate that NMPSO has superior performance over four current MOPSOs, and over four competitive multiobjective evolutionary algorithms (SPEA2-SDE, NSGA-III, MOEA/DD, and SRA), when solving most of the test problems adopted.

Verifiable pattern matching is the problem of finding a given pattern verifiably from the outsourced textual data, which is resident in an untrusted remote server. This problem has drawn much attention due to a large number of applications. The state-of-the-art method for this problem suffers from low efficiency. To enable fast verifiable pattern matching, we propose a novel scheme in this paper. Our scheme is based on an ordered set accumulator data structure and a newly developed verifiable suffix array structure, which only involves fast cryptographic hash computations. Our scheme also supports fast multiple-occurrence pattern matching. A striking feature of our proposed scheme is that our scheme works even with no secret keys, which ensures public verifiability. We conduct extensive experiments to evaluate the proposed scheme using Java. The results show that our scheme is orders of magnitude faster than the state-of-the-art work. Specifically, our scheme with public verifiability only costs a preprocessing time of 47 s (merely one-time off-line cost during outsourcing), a search time of 30 μs, a verification time of 149 μs, and a proof size of 2760 bytes for a verifiable pattern matching query with pattern length 200 on 10-million long textual data which consists of sequences of two-byte, Unicode characters in Java.

Data outsourcing is a promising service for data owners, where their data are stored on a cloud storage provider. Since the cloud is not fully trusted, data access control has become a challenging issue in the Cloud Storage System (CSS). Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a feasible technique for ensuring access control in the CSS, where an attribute authority is responsible to manage attributes and distribute keys. In this paper, we propose a novel revocable Multi-Authority CP-ABE scheme, in which the access policy can be constructed as an arbitrary tree rather than a matrix used by existing schemes. The tree-like policy makes our scheme more flexible. Consequently, the encryption, decryption and attribute revocation operations are also more efficient. Our scheme is also proved to be secure under the standard assumption. It can resist user collusion attack, while the attribute revocation operation also achieves both forward security and backward security. Simulation results show that our scheme is highly efficient.

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

In recent years, multiobjective immune algorithms (MOIAs) have shown promising performance in solving multiobjective optimization problems (MOPs). However, basic MOIAs only use a single hypermutation operation to evolve individuals, which may induce some difficulties in tackling complicated MOPs. In this paper, we propose a novel hybrid evolutionary framework for MOIAs, in which the cloned individuals are divided into several subpopulations and then evolved using different evolutionary strategies. An example of this hybrid framework is implemented, in which simulated binary crossover and differential evolution with polynomial mutation are adopted. A fine-grained selection mechanism and a novel elitism sharing strategy are also adopted for performance enhancement. Various comparative experiments are conducted on 28 test MOPs and our empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving MOPs of different types.

Ciphertext-policy attribute-based encryption (CP-ABE) is a very promising encryption technique for secure data sharing in the context of cloud computing. Data owner is allowed to fully control the access policy associated with his data which to be shared. However, CP-ABE is limited to a potential security risk that is known as key escrow problem, whereby the secret keys of users have to be issued by a trusted key authority. Besides, most of the existing CP-ABE schemes cannot support attribute with arbitrary state. In this paper, we revisit attribute-based data sharing scheme in order to solve the key escrow issue but also improve the expressiveness of attribute, so that the resulting scheme is more friendly to cloud computing applications. We propose an improved two-party key issuing protocol that can guarantee that neither key authority nor cloud service provider can compromise the whole secret key of a user individually. Moreover, we introduce the concept of attribute with weight, being provided to enhance the expression of attribute, which can not only extend the expression from binary to arbitrary state, but also lighten the complexity of access policy. Therefore, both storage cost and encryption complexity for a ciphertext are relieved. The performance analysis and the security proof show that the proposed scheme is able to achieve efficient and secure data sharing in cloud computing.

Ciphertext-policy attribute-based encryption (CP-ABE) has been a preferred encryption technology to solve the challenging problem of secure data sharing in cloud computing. The shared data files generally have the characteristic of multilevel hierarchy, particularly in the area of healthcare and the military. However, the hierarchy structure of shared files has not been explored in CP-ABE. In this paper, an efficient file hierarchy attribute-based encryption scheme is proposed in cloud computing. The layered access structures are integrated into a single access structure, and then, the hierarchical files are encrypted with the integrated access structure. The ciphertext components related to attributes could be shared by the files. Therefore, both ciphertext storage and time cost of encryption are saved. Moreover, the proposed scheme is proved to be secure under the standard assumption. Experimental simulation shows that the proposed scheme is highly efficient in terms of encryption and decryption. With the number of the files increasing, the advantages of our scheme become more and more conspicuous.

Abstract User's security and privacy are core issues of network applications. This paper proposes a privacy protection model to evaluate property risk of users, which takes into account both sensitivity of the property and requester's level of assurance. Furthermore, the sensitivity of the property is evaluated by problem of expectation–maximization algorithm. Experiments show that the optimal size of samples for EM is relatively small, which indicates the high efficiency of the algorithm. The proposed privacy protection model cannot only assist users to do correct authorization, but also benefit the privacy reservation of users in online service. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACT With fast development of cloud computing, more and more sensitive data are stored in distributed storage systems. It is mainly suffered from the following two threats: (i) data at rest are stolen; and (ii) data in transmission are intercepted. The threats may lead to serious problems. For example, personal private data eavesdropping may lead to legal problems and credit crisis, while company information leaking may cause huge economic loss. To tackle such security threat in cloud distributed storage system, we propose a novel scheme to produce dynamic size of shares with multiple iterations of secret sharing scheme. A file is partitioned into multiple file shares, and one of them is taken as a new file to be further partitioned into smaller size with low additional computational cost and data expansion. The small‐size file share is stored at user terminal as a necessary component to recover original file. It can be used in scenarios that user terminal, such as smart phone, is one of nodes in distributed storage systems. In this case, attackers cannot recover information through eavesdropping shares transmitted in network. Storage service providers cannot also obtain useful information through file shares stored online without the participant of the small‐size file share at user terminal. Therefore, sensitive data can be protected confidentially. Copyright © 2013 John Wiley & Sons, Ltd.

With cloud computing and mobile computing becoming more and more popular, there are a lot potential applications for computation outsourcing to the cloud. This paper investigates the linear regression outsourcing problem, which is a quite common engineering task and employed in various applications, as a case study to find out the possible problems that need to be solved. We propose two protocols which can enable secure and efficient outsourcing of linear regression problems to the cloud. The protocols can protect the client’s data privacy well and at the same time have good efficiency. We show all subtleties and the techniques in designing such protocols. The main idea to protect the privacy is employing some transformations to the original linear regression problem to get a new problem which is sent to the cloud; and then transforming the answer returned back from the cloud to get the true solution to the original problem. Experimental results validate the practical usability of our protocols.

Nurse rostering is a non-deterministic polynomial problem with many constraints. In the literature, a number of heuristic approaches have been proposed, but few of them can achieve satisfying performance on both solution quality and search speed. Inspired by the successful experience of ant colony optimization (ACO) on many highly-constrained problems, this paper proposed an ant colony optimization approach termed ACO-NR for solving the nurse rostering problem. First, the search space of the nurse rostering problem is remodeled as a graph, with each solution corresponding to a path on the graph. Then a heuristic function is designed to guide the path construction behavior of ACO-NR. The heuristic information comes not only from the static information defined by the problem-dependent knowledge, but also from the dynamic information generated by the solution construction procedure. A penalty function is defined to help ACO-NR handle problem constraints. Experimental results on 52 benchmark instances show that the proposed ACO-NR can achieve better performance than classic nurse rostering algorithms.