The rapid growth of Electric Vehicles (EVs) has brought significant challenges in ensuring the privacy of sensitive data generated, particularly in Vehicle-to-Vehicle (V2V) energy trading systems. This study examines methods to balance data privacy preservation with the utility required for EV-related services. Existing privacy-preserving techniques often struggle to strike a balance between privacy and utility, particularly in dynamic environments where data sensitivity and usage patterns are constantly changing. In this paper, we propose an Adaptive Private Stable Matching (APSM) algorithm that incorporates a dynamic privacy budget algorithm for Differential Privacy (DP). APSM provides stable, privacy-preserving matches for EVs participating in V2V energy trading. The dynamic privacy budget mechanism adjusts allocation according to the number of EVs, offering enhanced privacy protection when necessary and increased utility when feasible. The proposed approach optimizes the utilization of the privacy budget, meeting both strict privacy requirements and ensuring efficient service delivery. Experimental results show that the technique outperforms static approaches in terms of privacy budget management, thereby enhancing privacy protection while maintaining high data utility. This combination renders APSM highly suitable for practical V2V energy trading scenarios, delivering robust privacy safeguards without compromising system performance.

With the continuous development of industrial manufacturing, rationally scheduling multiple-source flexible orders is crucial for achieving sustainable production. However, most existing methods adopt a single-task optimization paradigm with independent modeling, overlooking the similarities among multi-source orders and the correlations between scheduling optimizations. This leads to additional waste of computational resources and the loss of prior experience. To address this issue, we propose a many-objective multi-task optimization paradigm that takes into account the correlations among multi-source flexible orders. Specifically, through the multi-task optimization paradigm, multiple flexible orders with different variable dimensions are uniformly modeled by sharing a set of objective functions, thereby enabling the joint optimization of related scheduling tasks. Secondly, to investigate the impact of tool wear on scheduling performance, an exponential function-based tool wear coefficient is introduced, and a many-objective optimization model is constructed that considers indicators such as processing time, energy consumption, load balancing, and tool wear amount. Next, considering the discrete problem properties, a many-objective multi-task evolutionary algorithm based on load balancing (MTMOEA-LB) is designed to avoid premature convergence of the multi-task optimization algorithm due to task differences. Finally, the actual production order data set of a flange enterprise is used for experimental comparison. Experimental results indicate that the proposed algorithm can output high-quality scheduling schemes quickly and effectively compared with other advanced optimization algorithms.

Set-valued data, an important data form expressing diversity and uncertainty, is widely used in fields such as recommendation systems and social network analysis. However, such data often contains fine-grained records, which may lead to the leakage of users’ sensitive information. To this end, some privacy-preserving set-valued data analysis schemes have been proposed. This paper first proposes a joint shift inference attack against the cyclic shift-based local differential privacy (LDP) protocol introduced by Huang et al. (10.1109/TIFS.2024.3423657), it exploits deterministic cyclic-shift patterns and significant frequency differences to infer the user’s original data. Experimental results demonstrate that the user’s original data can be inferred with a probability exceeding 96%. Theoretically, the cyclic-shift mechanism violates the core requirement of local differential privacy due to its non-surjective output space. To overcome the limitations of existing schemes, we propose a privacy-preserving joint distribution analysis scheme for set-valued data via LDP (SVJDA). It employs the Sparse Vector Mean Estimation (SVME) mechanism and utilizes a sign-based hashing function to compress data, allowing privacy-preserving joint distribution analysis while introducing only minimal additional noise. Theoretical analysis shows that SVJDA satisfies ϵ-LDP with minimal estimation error. The experimental results confirm that SVJDA achieves higher accuracy in joint distribution estimation while ensuring the accuracy of frequent itemset identification. For ϵ ∈ [0.4, 1], the L∞ error of SVJDA is only 7.208%–21.725% of SVSM and 2.821%–7.279% of LDP-RM, while its MSE is 0.00364%–2.338% of SVSM and 0.017%–0.068% of LDP-RM, demonstrating its superior performance.

The secure deployment of Federated Learning (FL) is critically undermined by statistical data heterogeneity and a profound vulnerability to adversarial attacks, these weaknesses are exacerbated by FL’s privacy-preserving preclusion of large-scale, centralized data for robust training. Existing proto-typebased methods suffer from representation collapse when naively aggregating from non-IID clients, while generative approaches often lack a principled mechanism for synthesizing features that confer adversarial resilience. We introduce Federated Contrastive Diffusion Prototypes (Fed-CDP), a novel paradigm that transforms the server from a passive aggregator into an active synthesis hub for robust features. Fed-CDP aggregates lightweight client prototypes to serve as semantic anchors, guiding a server-side diffusion model via a contrastive objective. This process synthesizes a high-fidelity feature space explicitly optimized for maximal inter-class separability, a property intrinsically linked to robust generalization. These server-generated features are then distributed to clients as a potent regularizer, aligning disparate local models and directly mitigating client drift. Our extensive evaluations across multiple challenging datasets establish that Fed-CDP outperforms existing state-of-the-art baselines. For instance, on CIFAR-100 under severe heterogeneity (α = 0.1), Fed-CDP surpasses leading methods by nearly 5% in standard accuracy and over 9% in robust accuracy under Projected Gradient Descent attacks. Fed-CDP provides a new blueprint for building secure and high-performance collaborative AI, laying the foundation for trustworthy systems in critical sectors like finance and multi-institutional healthcare.

The desire to overcome reliability issues of the distributed energy resources (DERs) led researchers to develop a novel concept named virtual power plant (VPP). VPPs are supposed to carry out intelligent and secure energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain within a decentralized VPP network emerged as a novel paradigm. However, this decentralization also suffers with trust, reliability, energy management, and efficiency issues due to DERs dynamic nature. Thus, in this article, we first work to provide an efficient energy management strategy for VPPs to enhance demand response, then we propose an energy oriented trading and block mining protocol and name it as P roof o f E nergy M arket (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a P rivate PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as V irtual P rivate T rading (VPT). We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading-time profit values. Afterwards, we carry out simulation-based experiments of our proposed model to show the effectiveness and novelty as compared to state-of-the-art works.

Accurately detecting out-of-scope queries is a challenging task in task-oriented dialog systems. Most existing research focus on adding an outlier detector after classification or designing an open world classification to identify unknown intents. There is still a major performance gap on achieving high efficiency and accuracy based on above methods. In our research, we tend to solve this problem by constructing an out-of-scope class in the classification. We propose an explainable centroid-based fine-tuning method including a modified decision metric (MDM) and a centroid-based cosine loss (CCL) on Pre-trained Transformer models for optimization. This loss function builds on Copernican structure and assigns the same margin to each in-scope class to resolve an ambiguous configuration on out-of-scope detection. Moreover, cosine similarity is utilized to remove radial variations of centroids. Experimental results show that our proposed method achieves improvement compared to other baseline methods.

In FL-assisted Internet of Vehicles (IoV) system, joint vehicle scheduling and resource management have become an effective approach to improving Federated Learning (FL) communication efficiency. However, heterogeneity in data, varying computational capabilities, and limited resources among different vehicle devices lead to significant disparities in how frequently devices participate in FL and in their training performance. This generates unfairness during device selection, which in turn constrains overall system performance and the model’s generalization ability. To address these challenges, we propose a fairness-aware interval constrained many-objective optimization method for joint vehicle scheduling and bandwidth allocation. The proposed method considers factors such as device accuracy, latency, server utilization, and fairness in vehicle participation while satisfying energy consumption constraints. During FL parameter aggregation, a fairness-driven aggregation strategy is designed, which adjusts aggregation weights based on a fairness indicator. Additionally, we develop an indicator-driven multi-level interval constrained many-objective evolutionary algorithm (IM-ICMaOEA) to efficiently identify the Pareto Front that satisfies the constraints. Extensive simulation results demonstrate the effectiveness of the proposed method in terms of accuracy, device delay, energy consumption, and fairness in vehicle participation, significantly enhancing training efficiency in wireless channel environments. It provides significant scientific value and application potential in the field of the IoV.

Differential private (DP) query and response mechanisms have been widely adopted in various applications based on Internet of Things (IoT) to leverage variety of benefits through data analysis. The protection of sensitive information is achieved through the addition of noise into the query response which hides the individual records in a dataset. However, the noise addition negatively impacts the accuracy which gives rise to privacy-utility tradeoff. Moreover, the DP budget or cost$\epsilon $is often fixed and it accumulates due to the sequential composition which limits the number of queries. Therefore, in this article, we propose a framework known as optimized privacy-utility tradeoff framework for data sharing in IoT (OPU-TF-IoT). First, OPU-TF-IoT uses an adaptive approach to utilize the DP budget$\epsilon $by considering a new metric of population or dataset size along with the query. Second, our proposed heuristic search algorithm (HSA) reduces the DP budget accordingly whereas satisfying both data owner and data user. Third, to make the utilization of DP budget transparent to the data owners, a blockchain-based verification mechanism is also proposed. Finally, the proposed framework is evaluated using real-world datasets and compared with the traditional DP model and other related state-of-the-art works. The results demonstrate that our proposed framework not only utilizes the DP budget$\epsilon $efficiently, but also optimizes the number of queries by 49% and 54% on average compared to state-of-the-art and standard DP models, respectively. Furthermore, the data owners can effectively make sure that their data are shared accordingly through our blockchain-based verification mechanism which encourages them to share their data into the IoT system.

The recent increase in Electric Vehicles (EVs) on the road has highlighted privacy concerns, particularly in the Vehicle-to-Vehicle (V2V) energy trading scenario. Ensuring location privacy in Vehicular Ad Hoc Networks (VANETs) is crucial for user confidentiality. Existing privacy techniques in the V2V paradigm protect the location coordinates of the EVs, but privacy risks persist after EVs are matched. In this paper, we introduce a dual-layer location privacy matching (DLLPM) technique to enhance the privacy of V2V matching. Our approach utilizes Laplace differential privacy and partial homomorphic encryption , ensuring that the EV’s private data remains inaccessible to both participants and adversaries. We introduce a noise addition and clipping algorithm to obfuscate EV coordinates within a defined radius. Encrypted distance-based preference lists are generated using partial homomorphic encryption to establish differentially private stable matches. DLLPM ensures EV location privacy throughout the matching process and mitigates the risk of location privacy leakage even after suppliers and demanders exchange location information . Theoretical analysis and experimental results confirm the efficiency of DLLPM, demonstrating robust privacy preservation with a computational complexity of O ( n 2 log n ⋅ ( C enc + C addHE + C subHE + C dec ) ) . We further evaluate computational performance using 128-bit and 256-bit encryption, showing that DLLPM achieves private and efficient matching in the V2V trading paradigm.

Notary cross-chain transaction technologies have obtained broad affirmation from industry and academia as they can avoid data islands and enhance chain interoperability. However, the increased privacy concern in data sharing makes the participants hesitate to upload sensitive information without the trust foundation of the external network. To address this issue, this paper proposes a differential private notary mechanism (DPNM) to preserve privacy in blockchain interoperations. It establishes a fully trusted notary organization to conduct data perturbation before replying query to the external blockchain network. In addition, the DPNM contains two built-in privacy budget allocation schemes: Efficiency priority scheme (EPS) and Privacy priority scheme (PPS). These schemes unify the privacy preferences among different nodes based on multi-node consensus in the decentralized environment. The EPS can generate noise linearly and work efficiently, and the PPS reflects better on nodes’ preferences. This paper utilizes several metrics including mechanism errors, elapsed time, latency, and gas consumption to evaluate the performance of DPNM compared to the traditional mechanisms. The experiment results indicate that the proposed mechanism can meet privacy preferences among different nodes and provide better utility with little extra cost.

The post-processed Differential Privacy (DP) framework has been routinely adopted to preserve privacy while maintaining important invariant characteristics of datasets in data-release applications such as census data. Typical invariant characteristics include non-negative counts and total population. Subspace DP has been proposed to preserve total population while guaranteeing DP for sub-populations. Non-negativity post-processing has been identified to inherently incur fairness issues. In this work, we study privacy and unfairness (i.e., accuracy disparity) concerns in the post-processed DP framework. On one hand, we propose the post-processed DP framework with both non-negativity and accurate total population as constraints would inadvertently violate privacy guarantee desired by it. Instead, we propose thepost-processed subspace DP frameworkto accurately define privacy guarantees against adversaries. On the other hand, we identify unfairness level is dependent on privacy budget, count sizes as well as their imbalance level via empirical analysis. Particularly concerning is severe unfairness in the setting of strict privacy budgets. We further trace unfairness back touniform privacy budget setting over different population subgroups. To address this, we propose avarying privacy budget settingmethod and develop optimization approaches using ternary search and golden ratio search to identify optimal privacy budget ranges that minimize unfairness while maintaining privacy guarantees. Our extensive theoretical and empirical analysis demonstrates the effectiveness of our approaches in addressing severe unfairness issues across different privacy settings and several canonical privacy mechanisms. Using datasets of Australian Census data, Adult dataset, and delinquent children by county and household head education level, we validate both our privacy analysis framework and fairness optimization methods, showing significant reduction in accuracy disparities while maintaining strong privacy guarantees.

Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine whether the inner product of two vectors satisfies a specific relation with 0 without leaking the vectors’ information. Next, we propose a privacy-preserving reverse dominance checking scheme (PRDC) based on symmetric homomorphic encryption. Finally, we achieve ePRSQ based on PIPSD and PRDC. Security analysis shows that PIPSD and PRDC are both secure in the real/ideal world model, and ePRSQ can protect the security of the dataset, the privacy of query requests and query results. Extensive experiments show that ePRSQ is efficient. Specifically, for a 3-dimensional dataset of size 1000, the computational and communication overheads of ePRSQ for a query are 79.47 s and 0.0021 MB, respectively. The efficiency is improved by$3.78\times$(300.58 s) and$928.57\times$(1.95 MB) respectively compared with PPARS, and by$61.31\times$(4872.55 s) and$407309\times$(855.35 MB) respectively compared with OPPRS.

With the rapid development of cloud computing and service computing, service recommendation systems play a crucial role in helping users efficiently filter the appropriate services. However, the sparsity of service data and the presence of noise in interactions make it extremely challenging to accurately capture user preferences. Existing service recommendation methods based on Graph Neural Networks (GNNs) primarily rely on ID aggregation, often neglecting the richness of textual semantics and are susceptible to interaction noise, resulting in suboptimal modeling of user-service relationships. Although Large Language Models (LLMs) demonstrate remarkable advantages in capturing textual semantics, current methods struggle to effectively align structural representations with textual representations, limiting improvements in recommendation performance. To address these challenges, we propose an innovative multi-semantic graph diffusion model for service recommendation, DiffMSR, which aims to align textual and structural representations while learning the generation process of interaction graphs in a denoising manner. This approach mitigates data sparsity and effectively reduces noise interference. Specifically, the model leverages LLMs to capture the textual semantic features of service descriptions and integrates them with structured semantic information from knowledge graphs. Through cross-semantic contrastive learning, it achieves heterogeneous semantic alignment. Furthermore, the model introduces a multisemantic diffusion-based generation framework, which iteratively denoises to construct high-quality user-service interaction graphs. This significantly enhances the multi-semantic awareness of user representations, thereby improving recommendation performance. Experiments on public service datasets demonstrate that DiffMSR outperforms existing state-of-the-art baseline methods, achieving improvements of 4.13% and 6.37% in recommendation accuracy and recall, respectively.

The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, traditional differentially private mechanisms tend to produce unbounded outputs in order to achieve maximum disturbance range, which is not always in line with real-world applications. Existing solutions attempt to address this issue by employing post-processing or truncation techniques to restrict the output results, but at the cost of introducing bias issues. In this paper, we propose a novel differentially private mechanism which uses a composite probability density function to generate bounded and unbiased outputs for any numerical input data. The composition consists of an activation function and a base function, providing users with the flexibility to define the functions according to the DP constraints. We also develop an optimization algorithm that enables the iterative search for the optimal hyper-parameter setting without the need for repeated experiments, which prevents additional privacy overhead. Furthermore, we evaluate the utility of the proposed mechanism by assessing the variance of the composite probability density function and introducing two alternative metrics that are simpler to compute than variance estimation. Our extensive evaluation on three benchmark datasets demonstrates consistent and significant improvement over the traditional Laplace and Gaussian mechanisms. The proposed bounded and unbiased composite differentially private mechanism will underpin the broader DP arsenal and foster future privacy-preserving studies.

Stable operation of IoT systems in extreme environments is critical for infrastructures, including energy, aerospace, and healthcare. But resource-constrained devices are vulnerable to malware exploiting known weaknesses due to complex operating conditions and low-frequency device maintenance, and systems are at serious risk of failure or information leakage. However, automated security defense based on machine learning faces the challenges of low cross-architectural generalization and multidimensional data imbalance. We propose a many-objective-optimization-driven data balancing strategy for cross-architectural malware classification (MODSC) to address the above issues. MODSC constructs the optimization problem model for data balancing strategy search based on data set information, which focuses on rebalancing the data space in different dimensions, including category distribution and architectural distribution. The many-objective evolutionary algorithm (MaOEA-PBF), guided by the performance balance function, is then designed to solve the above model. The MODSC framework was tested on a group of IoT malware data sets with stepped imbalance rates containing six common architectures. The experimental results show that MODSC can both steadily and effectively improve cross-architectural generalization while maintaining a high level of confidence compared to popular data processing methods.

The integration of permissioned blockchain such as Hyperledger fabric (HF) and Industrial internet of Things (IIoT) has opened new opportunities for interdependent supply chain partners to improve their performance through data sharing and coordination. The multichannel mechanism, private data collection and querying mechanism of HF enable private data sharing, transparency, traceability, and verification across the supply chain. However, the existing querying mechanism of HF needs further improvement for statistical data sharing because the query is evaluated on the original data recorded on the ledger. As a result, it gives rise to privacy issues such as leaking of business secrets, tracking of resources and assets, and disclosing of personal information. Therefore, we solve this problem by proposing a differentially private enhanced permissioned blockchain for private data sharing in the context of supply chain in IIoT which is known as (EDH-IIoT). First, we integrate differential privacy into the chaincode (smart contract) of HF which evaluates the query and adds a calibrated noise into it. Second, we propose an algorithm to efficiently utilize ϵ through reuse of the privacy budget for the repeated queries. Third, we also propose an algorithm to track the privacy budget (ϵ) and avoid the degrade of privacy preservation in case of multiple queries on the same portion of the ledger's data. Furthermore, the reuse and tracking of ϵ enables the data owner to ensure that ϵ does not exceed the threshold which is the maximum privacy budget (ϵt). Finally, we model two privacy attacks namely linking attack and composition attack to evaluate and compare privacy preservation, and the efficiency of reuse of ϵ with the default chaincode of HF and traditional differential privacy model, respectively. The results confirm that EDH-IIoT obtains an accuracy of 97% in the shared data for ϵ = 1, and a reduction of 35.96% in spending of ϵ.

With the wide use of GPS enabled devices and Location-Based Services, location privacy has become an increasingly worrying challenge to our community. Existing approaches provide solid bases for addressing this challenge. However, they mainly focus on independent location perturbation or whole trajectory perturbation with little attention to location dependency based perturbation between two successive locations. This may result in that perturbed successive locations lose mutual distance and direction dependency, which in turn can lead to significant data utility loss. To address this problem, we propose a new location dependency based privacy notion named as vector-indistinguishability (vector-ind). Vector-ind defines a vector to represent the dependency relationship between two successive locations. Correspondingly, it consists of distance-indistinguishability for distance dependency and direction-indistinguishability for direction dependency. Noise generation for applying Differential Privacy is based on the distance and direction, hence can reflect the location dependency to ensure data utility after perturbation. We also present four mechanisms to achieve vector-ind notion. Finally, we evaluate the empirical privacy and utility of vector-ind in real-world GPS data to demonstrate how our proposed mechanisms for vector-ind can protect location privacy with high data utility in successive location data.

This article establishes an edge computing architecture based on blockchain sharding to apply blockchain technology at the edge layer. The tasks offloaded to the edge layer are performed by a set of edge server nodes for consensus operations, which improves the security of offloading. At the same time, the nodes and offloaded tasks are divided into multiple shards, and the typical practical Byzantine fault tolerance (PBFT) consensus is performed in each shard to improve scalability. In order to avoid the impact of malicious nodes in an unreasonable sharding scheme on network security and performance, we model the many-objective optimization sharding problems, which includes reducing consensus latency, energy consumption, sharding failure probability, and improving sharding throughput. Moreover, we design a many-objective evolutionary algorithm based on information entropy (MaOEA-IE) to solve the model. In the process of population environment selection, the distribution information of individuals is mapped to a Gaussian function, and we aggregate the distribution functions of all individuals and calculate the information entropy to measure the distribution of the population. The simulation results of benchmark test problems and model solving problems show that the solution set of MaOEA-IE has the best convergence, diversity, and comprehensive performance. This article provides an idea for the formulation of blockchain sharding scheme.

Blockchain has gradually attracted widespread attention from the research community of the IoT, due to its decentralization, consistency, and other attributes. It builds a secure and robust system by generating a backup locally for each participant node to collectively maintain the network. However, this feature brings some privacy concerns since all nodes can access the chain data, users’ sensitive information under risk of leakage. The local differential privacy (LDP) mechanism can be a promising way to address this issue as it implements data perturbation before uploading to the chain. While traditional LDP mechanisms cannot fit well with the blockchain since the requirements of a fixed input range, large data volume, and using the same privacy budget, which are practically difficult in a decentralized environment. To overcome these problems, we propose a novel LDP mechanism to split input numerical data and implement perturbation by digital bits, which does not require a fixed input range and large data volume. In addition, we use an iteration approach to adaptively allocate the privacy budget for different perturbation procedures that minimize the total deviation of perturbed data and increase the data utility. We employ mean estimation as the statistical utility metric under the same and randomized privacy budgets to evaluate the performance of our novel LDP mechanism. The experiment results indicate that the proposed LDP mechanism performs better in different scenarios, and our adaptive privacy budget allocation model can significantly reduce the deviation of the perturbation function to provide high data utility while maintaining privacy.

As an application area of social computing, social recommendation aims to exploit the richness of social relationships to improve recommendation accuracy. Current research mainly considers social information that can be directly observed, with little attention to indirect social relationships between users or the explainability of social recommendation results. To address this challenge, this article proposes a knowledge graph-based many-objective model for explainable social recommendation (KGMESR) by considering the explainability, accuracy, novelty, and content quality of social recommendation results. The model takes advantage of social behavior information to calculate user similarity and quantifies the explainability of social recommendation results using entity vectors and embedding vectors. To ensure model efficiency, a many-objective recommendation algorithm based on the partition deletion strategy is designed. It employs the association of individuals with the nearest reference vector to render the diversity of the population and then obtains the final solution by deleting poorly converged individuals in each partition. Experimental results show that many-objective optimization recommendation algorithm based on partition deletion strategy (MaOEA-PDS) allows for preferable recommendation results for users in real datasets. The explainability of the proposed model is demonstrated by two case studies.

In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay the minimum possible amount along with getting maximum benefits. In this context, usage based dynamic pricing strategies of DSM plays their role and provide users with specific incentives that help shaping their load curve according to the forecasted load. However, these reported real-time values can leak privacy of smart meter users, which can lead to serious consequences such as spying, etc. Moreover, most dynamic pricing algorithms charge all users equally irrespective of their contribution in causing peak factor. Therefore, in this paper, we propose a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate the concept of differential privacy to protect the privacy of real-time smart metering data. To calculate accurate billing, we also propose a noise adjustment method. Finally, we proposeDemandResponse enhancingDifferentialPricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. We also carry out theoretical analysis for differential privacy guarantees and for cooperative state probability to analyze behavior of cooperative smart meters. The performance evaluation of DRDP strategy at various privacy parameters show that the proposed strategy outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.11.A preliminary version has been published by 2020 IEEE International Conference on Communications (ICC 2020), June, 2020, Dublin, Ireland entitled Differentially Private Dynamic Pricing for Efficient Demand Response in Smart Grid.

Using Web APIs registered in service sharing communities for mobile APP development can not only reduce development period and cost, but also fully reuse state-of-the-art research outcomes in broad domain so as to ensure up-to-date APP development and applications. However, the big volume of available APIs in Web communities as well as their differences make it difficult for APIs selection considering compatibility, preferred partial APIs and expected APIs functions which are often of high variety. Accordingly, how to recommend a set of functional-satisfactory and compatibility-optimal APIs based on the APP developer's multiple function expectation and pre-chosen partial APIs is on demand as a significant challenge for successful APP development. To address this challenge, we first construct a Web APIs correlation graph that incorporates functional descriptions and compatibility information of Web APIs, and then propose a correlation graph-based approach for personalized and compatible Web APIs recommendation in mobile APP development. Finally, through extensive experiments on a real dataset crawled from Web APIs websites, we prove the feasibility of our proposed recommendation approach.

Directional distribution analysis has long served as a fundamental functionality in abstracting dispersion and orientation of spatial datasets. Spatial datasets that describe sensitive information of individuals such as health status and home addresses must be used and shared cautiously to protect individuals' privacy. There is an inherent tension between the need of accurate directional distribution result and the requirement of individuals' location privacy. Plenty of excellent location privacy protection approaches such as geo-indistinguishability can provide strong protection for locations but considerably at the expense of statistical quality of subsequent directional distribution analysis. In this paper, to protect individual location data for directional distribution, we define the geographic feature of community with covariance matrix and then propose ageo-ellipse-indistinguishabilityprivacy notion incorporating this covariance matrix. As an instantiation of metric differential privacy,geo-ellipse-indistinguishabilityguarantees pairwise inputs cannot be distinguishable with the level proportional to privacy budget and Mahalanobis distance between them, given a randomized output. We also present elliptical privacy mechanisms to achieve this privacy definition on the basis of gamma distribution and multivariate normal distribution. We finally evaluate the empirical utility of the proposed mechanism in New York home addresses database. Our experiments demonstrate that under the same privacy level, our proposed elliptical approach can achieve significantly higher directional distribution utility than circular noise function based method.

Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requires a model to forget about a training data sample if requested by the data owner (i.e., machine unlearning). As an essential step prior to machine unlearning, it is still a challenge for a data owner to tell whether or not her data have been used by an unauthorized party to train a machine learning model. Membership inference is a recently emerging technique to identify whether a data sample was used to train a target model, and seems to be a promising solution to this challenge. However, straightforward adoption of existing membership inference approaches fails to address the challenge effectively due to being originally designed for attacking membership privacy and suffering from several severe limitations such as low inference accuracy on well-generalized models. In this paper, we propose a novel membership inference approach inspired by the backdoor technology to address the said challenge. Specifically, our approach of Membership Inference via Backdooring (MIB) leverages the key observation that a backdoored model behaves very differently from a clean model when predicting on deliberately marked samples created by a data owner. Appealingly, MIB requires data owners' marking a small number of samples for membership inference and only black-box access to the target model, with theoretical guarantees for inference results. We perform extensive experiments on various datasets and deep neural network architectures, and the results validate the efficacy of our approach, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference.

Industrial cyber–physical–social systems (CPSSs), a prominent data-driven paradigm, tightly couple and coordinate social space into cyber–physical systems (CPSs) within industrial environments. With the proliferation of cloud–fog computing, cloud–fog computing becomes the most prominent computing paradigm used to implement industrial data analysis. However, the open environment of cloud–fog computing and the limited control of industrial CPSSs users make industrial data analysis without compromising users’ privacy one great research challenge in practical cloud–fog-based industrial applications. High-order Bi-Lanczos (HOBI-Lanczos) approach has shown remarkable success in heterogeneous data analysis in industrial applications. In this article, a novel privacy preserving HOBI-Lanczos approach using tensor train in cloud–fog computing is proposed for industrial data applications. Specifically, a privacy preserving industrial data analysis model using cloud–fog computing and tensor train is firstly proposed. The proposed model enables fogs and clouds to securely carry out industrial data analysis for large-scale tensors given in a tensor train format. In addition, by using this model, a privacy preserving HOBI-Lanczos approach is provided. Last but not least, by using a brain-controlled robot system case study, the proposed approach is theoretically and empirically analyzed. Our proposed approach is proven to be secure. A series of experiments corroborate the superiority of the proposed approach in cloud–fog computing for industrial applications.

Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

Summary With the increasing popularity of multimedia technology and the prevalence of various smart electronic devices, severe security problems have recently arisen in information and communication systems. The image maintenance can be treated as a typical example of cloud storage outsourcing as images require much more storage space than text documents. An image encryption method with high efficiency is crucial to preserve the privacy of sensitive and critical images in cloud‐edge communications. Compressive sensing (CS) is capable of reconstructing compressible or sparse signals using fewer measurements than traditional methods when the support of the signals is unobtainable in advance. Encryption methods based on CS decrease resources demands during signal acquisition and protect the image data to avoid theft of confidential information by malicious users. Moreover, the CS‐based encryption method can achieve the encryption and the compression of an image simultaneously. Recently, a considerable amount of work using CS theory has been reported. However, most of the existing methods result in recovered images with unsatisfactory quality and employ a massive measurement matrix as the encryption key. This paper proposes an encryption scheme for images on cloud based on a single‐round dictionary and chaotic sequences. Both the experimental and analytical results verify the proposed scheme's effectiveness, high level of security, and substantial image quality improvement after decryption.

Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of low efficiency, and if the data are used maliciously, the security loss may be further caused. Multicloud is a high-performance secure computing platform, which combines multiple cloud providers for data processing, and the distributed multicloud platform ensures the security of data to some extent. Based on multicloud and task scheduling in IoT, this article constructs a many-objective distributed scheduling model, which includes six objectives of total time, cost, cloud throughput, energy consumption, resource utilization, and balancing load. Furthermore, this article presents a many-objective intelligent algorithm with sine function to implement the model, which considers the variation tendency of diversity strategy in the population is similar to the sine function. The experimental results demonstrate excellent scheduling efficiency and hence enhancing the security. This work provides a new idea for addressing the difficult problem of data processing in IoT.

The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and always transmitted to the cloud for analysis. In order to reduce cost and reply promptly, we deploy artificial intelligence (AI) models for data analysis on edge servers. Subspace clustering, the core of the AI model, is employed to analyze high-dimensional image data such as HSIs. However, most traditional subspace clustering algorithms construct a single model, which can be affected by noise more easily. It hardly balances the sparsity and connectivity of the representation coefficient matrix. Therefore, we proposed a postprocess strategy of subspace clustering for taking account of sparsity and connectivity. First, we define close neighbors as having more common neighbors and higher coefficients neighbors, where the close neighbors are selected according to the nondominated sorting algorithm. Second, the coefficients between the sample and close neighbors are reserved, incorrect, or useless connections are pruned. Then, the postprocess strategy can reserve the intrasubspace connection and prune the intersubspace connection. In experiments, we verified the universality and effectiveness of postprocessing strategies in the traditional image recognition field and IoT field, respectively. The experiment results demonstrate that the proposed strategy can process noise data in the IoT to improve clustering accuracy.

Cyber-physical-social big data concern heterogeneous, multiaspect, large-volume data generated in cyber-physical-social systems (CPSS). Orthogonal tensor SVD (OTSVD) has emerged as a powerful tool to reduce cyber-physical-social big data. In this work, we propose an improved secure high-order-Lanczos based OTSVD for cyber-physical-social big data reduction in clouds. Specifically, to take advantage of the parallel processing capability of cloud computing, the improved secure high-order Lanczos algorithm is derived by restructuring the original high-order Lanczos algorithm such that only one synchronization point per iteration is required. To protect data privacy, the improved secure high-order-Lanczos based OTSVD employs homomorphic encryption integrated with batching technique, and garbled circuits, and makes all computations of the OTSVD algorithm in clouds come true. To our knowledge, this is the first study to efficiently tackle big data reduction in clouds in a privacy-preserving manner. Finally, we prove that our improved approach is secure in semi-trusted model. And we evaluate the proposed improved secure OTSVD on real datasets. The results show that our proposed improved secure approach is efficient and scalable for cyber-physical-social big data reduction.

When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too inefficient for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.

The traditional coal energy system cannot meet the requirements of modern industrial production for sustainable development because extensive economic growth has caused significant coal consumption. Therefore, it is particularly important to formulate an effective coal production decision-making scheme to support complex coal production systems for sustainable development. To address this challenge, an improved many-objective fuzzy decision-making model with human participation for coal production prediction is established in this article. The model includes five objective functions: economic, energetic, environmental, coal gangue, and safety profits. Moreover, a novel multistage many-objective optimization algorithm is designed to adjust relevant model parameters and make the obtained model solution more accurate when compared with involved algorithms. In the algorithm, there are three stages of optimization. In stage one, the model with the relatively good convergence solution is chosen. In stage two, the diversity of the model solution needs to be retained by employing a diversity maintenance mechanism. In stage three, a comprehensive measurement approach is designed to ensure the balance of convergence and diversity during population evolution. To verify the validity of the model and the superiority of the optimization algorithm, five other advanced many-objective algorithms are used for comparison. The results from simulations on a benchmark function show that the proposed optimization algorithm has better performance than involved algorithms. Model solutions obtained on different metrics have better convergence and diversity for the coal fuzzy decision-making problem.

While the industrial Internet of Things (IIoT) can support efficient control of the physical world through large amounts of industrial data, data security has been a challenge due to various interconnections and accesses. Blockchain technology can support security and privacy preservation in IIoT data with its trusted and reliable security mechanism. Sharding technology can help improve the overall throughput and scalability of blockchain networks. However, the effectiveness of sharding is still challenging due to the uneven distribution of malicious nodes. By aiming to improve the performance of blockchain networks and reduce the possibility of malicious node aggregation, in this article, we propose a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize the shard validation validity model. Then, an optimal blockchain sharding scheme is obtained. Compared with other state-of-the-art many-objective optimization algorithms, MaOEA-DRP performs better on the DTLZ test suite. The simulation results demonstrate that our proposed algorithm can significantly improve the throughput and validity of sharding for better security in the blockchain-enabled IIoT.

There has been an increasing interest of integrating blockchain into cyber-physical systems (CPS). The design of password hashing schemes (PHSs) is in the core of blockchain security. However, no existing PHS seems to meet both the requirements of sufficient security and small code size for blockchain-based CPSs. In this article, a novel memory-hard PHS based on the classic PBKDF2 is proposed. Evaluation results show that the proposed scheme is promising for blockchain-based CPS, as it manages to provide enhanced security in comparison to PBKDF2 with limited increase in code size.

Cyber-physical-social systems (CPSS) are revolutionizing the relationships between humans, computers, and things. Outsourcing computation to the cloud can offer resources-constrained enterprises and consumers sustainable computing in CPSS. However, ensuring the security of data in such an outsourced environment remains a research challenge. Principal eigentensor computation has emerged as a powerful tool dealing with multidimensional cyber-physical-social systems data. In this paper, we present two novel secure principal eigentensor computation (SPEC) schemes for sustainable CPSS. To the best of our knowledge, this is the first effort to address SPEC over encrypted data in the cloud without the interaction need between multiple users and cloud. More specifically, we leverage cloud server and trusted hardware component to design a collaborative cloud model. Using the model, we propose (1) a basic SPEC scheme based on homomorphic computing and (2) an efficient SPEC scheme that combines the advantages of homomorphic computing and garbled circuits, and exploits packing technology to reduce computational cost. Finally, we theoretically and empirically analyze the security and efficiency of our SPEC schemes. Findings demonstrate that the proposed schemes provide a secure and efficient way of outsourcing computation for CPSS. In addition, from the cloud user's perspective, our proposal is lightweight.

Recent studies show that word embedding models often underestimate similarities between similar words and overestimate similarities between distant words.This results in word similarity results obtained from embedding models inconsistent with human judgment.Manifold learningbased methods are widely utilized to refine word representations by re-embedding word vectors from the original embedding space to a new refined semantic space.These methods mainly focus on preserving local geometry information through performing weighted locally linear combination between words and their neighbors twice.However, these reconstruction weights are easily influenced by different selections of neighboring words and the whole combination process is time-consuming.In this paper, we propose two novel word representation refinement methods leveraging isometry feature mapping and local tangent space respectively.Unlike previous methods, our first method corrects pre-trained word embeddings by preserving global geometry information of all words instead of local geometry information between words and their neighbors.Our second method refines word representations by aligning original and refined embedding spaces based on local tangent space instead of performing weighted locally linear combination twice.Experimental results obtained from standard semantic relatedness and semantic similarity tasks show that our methods outperform various state-of-the-art baselines for word representation refinement.

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0; if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.

Efficient load utilization in order to match electric supply is one of the most considered factors in smart grid management. Demand side management (DSM) strategies such as real-time dynamic energy pricing has motivated customers to efficiently use their energy in order to reduce their bills intelligently. However, this real-time dynamic pricing can be a threat to privacy of smart homes inhabitants, as their lifestyle can easily be revealed via these real-time electricity values. Therefore, a strong privacy preserving strategy needs to be incorporated with real-time pricing. In this paper, we propose a Differentially private demand Response enhancing Dynamic Pricing (DRDP) strategy that incorporates the advantages of differential privacy, and usage based dynamic billing. The proposed strategy effectively protects user's privacy along with enhancing dynamic pricing by incentivizing the participating smart homes.

Summary Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi‐objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non‐defective sampling and optimize the parameters of SVM. Then, three under‐sampled methods for decision region range are proposed to select the non‐defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G‐mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.

Summary Distance vector‐hop (DVHop), as a range‐independent positioning algorithm, is a significant positioning method in wireless sensor networks (WSNs). It is composed of three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning method results in a larger positioning error. Therefore, to enhance the positioning precision, this paper investigates the characteristic of error distribution between the estimated and real distance in the DVHop algorithm and reveals that the error is subjecting to the Gaussian distribution, N∼(0,1/3CR). Furthermore, to improve positioning accuracy, we propose a Gaussian error correction multi‐objective positioning model with non‐dominated sorting (NSGA‐II), which named GGAII‐DVHop. Finally, this model is tested on three complex network topologies, and the results demonstrate that it is significantly superior to other four algorithms in both positioning precision and robustness.

Summary The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real‐time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi‐instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real‐time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi‐instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time‐transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time‐series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi‐instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state‐of‐the‐art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.

Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests.

Big sensing data is commonly encountered from various surveillance or sensing systems. Sampling and transferring errors are commonly encountered during each stage of sensing data processing. How to recover from these errors with accuracy and efficiency is quite challenging because of high sensing data volume and unrepeatable wireless communication environment. While Cloud provides a promising platform for processing big sensing data, however scalable and accurate error recovery solutions are still need. In this paper, we propose a novel approach to achieve fast error recovery in a scalable manner on cloud. This approach is based on the prediction of a recovery replacement data by making multiple data sources based approximation. The approximation process will use coverage information carried by data units to limit the algorithm in a small cluster of sensing data instead of a whole data spectrum. Specifically, in each sensing data cluster, a Euclidean distance based approximation is proposed to calculate a time series prediction. With the calculated time series, a detected error can be recovered with a predicted data value. Through the experiment with real world meteorological data sets on cloud, we demonstrate that the proposed error recovery approach can achieve high accuracy in data approximation to replace the original data error. At the same time, with MapReduce based implementation for scalability, the experimental results also show significant efficiency on time saving.

Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing whole user information will result in a low recommendation efficiency. In addition, the traditional recommendation system ignores the inherent connection between user's preference and time. In reality, the interest of the user varies over time. Recommendation system should provide users accurate and fast with the change of time. To address this, we propose a novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-means), called TCCF. The clustering method can cluster similar users together for further quick and accurate recommendation. Moreover, an effective and personalized recommendation model based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It can provide a higher quality recommendation by analyzing the user's behaviors. The extensive experiments are conducted on two real datasets of MovieLens and Douban, and the precision of our model have improved about 5.2 percent compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.

Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for IoT is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model, including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios is realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication are realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more secure and private, we use differential privacy technique, which ensures that no adversary will be able to infer private information of any participant with confidence. Furthermore, to reduce computational complexity at every trading node, we use consortium blockchain, in which selected nodes are given authority to add a new block in the blockchain. Finally, we develop differentially private Energy Auction for bLockchain-based microgrid systems (DEAL). We compare DEAL with Vickrey-Clarke-Groves (VCG) auction scenario and experimental results demonstrates that DEAL outperforms VCG mechanism by maximizing sellers' revenue along with maintaining overall network benefit and social welfare.

Summary This special issue contains selected high‐quality papers from the 2016 International Conference on Information Technology in Medicine and Education (ITME2016), which was held on December 23‐25, 2016 in Fuzhou, China. The ITME conference series aims to provide an international forum for the presentation and discussion of research and development trends regarding information technology in medicine and education. The ITME2016 attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants. This special issue contains nine papers based on those that were presented at ITME2016. Among these are those by Liu et al, Lin et al, Zhang et al, Lin et al, Zhou et al, Cai et al, Li et al, Zheng et al, and Dai et al, 2018. Research problems in these papers have been analyzed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages. The papers were selected on this basis and also peer reviewed thoroughly. The nine papers reflect a vision of the area in state‐of‐art fashion.

With wide development of various information technologies, our daily activities are becoming deeply dependent on cyberspace. People often use handheld devices (e.g., mobile phones or laptops) to publish social messages, facilitate remote e-health diagnosis, or monitor a variety of surveillance. However, security insurance for these activities remains as a significant challenge. Representation of security purposes and their enforcement are two main issues in security of cyberspace. To address these challenging issues, we propose a cyberspace-oriented access control model (CoAC) for cyberspace whose typical usage scenario is as follows. Users leverage devices via network of networks to access sensitive objects with temporal and spatial limitations. We generalize subjects and objects in cyberspace and propose scene-based access control. To enforce security purposes, we argue that all operations on information in cyberspace are combinations of atomic operations. If every single atomic operation is secure, then the cyberspace is secure. Taking applications in the browser-server architecture as an example, we present seven atomic operations for these applications. A number of cases demonstrate that operations in these applications are combinations of introduced atomic operations. We also design a series of security policies for each atomic operation. Finally, we demonstrate both feasibility and flexibility of our CoAC model by examples.

The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

Resource constrained sensing devices are being used widely to build and deploy self-organizing wireless sensor networks for a variety of critical applications such as smart cities, smart health, precision agriculture and industrial control systems. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. A Data Stream Manager (DSM) at the server collects the data streams (often called big data) to perform real time analysis and decision-making for these critical applications. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to assure the trustworthiness of the collected data so that any decisions are made on the processing of correct data. Assuring high data trustworthiness requires that the system satisfies two key security properties: confidentiality and integrity. To ensure the confidentiality of collected data, we need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity of emerging applications or the sensed data types or the sensing devices. For example, a temperature in a precision agriculture application may not be as sensitive as monitored data in smart health. Providing multilevel data confidentiality along with data integrity for big sensing data streams in the context of near real time analytics is a challenging problem. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality and data integrity. Our method is based on two key concepts: common shared keys that are initialized and updated by DSM without requiring retransmission, and a seamless key refreshment process without interrupting the data stream encryption/decryption. Theoretical analyses and experimental results of our SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams.

The exponential growth of big data places a great burden on current computing environment. However, there exists a vast gap in the approaches that can securely and efficiently process the large scale heterogeneous data. This paper, on the basis of transparent computing paradigm, presents a unified approach that coordinates the transparent servers and transparent clients to decompose tensor, a mathematical model widely used in data intensive applications, to a core tensor multiplied with a number of truncated orthogonal bases. The structured, semi-structured as well as structured data are transformed to low-order sub-tensors, which are then encrypted using the Paillier homomorphic encryption scheme on the transparent clients. The cipher sub-tensors are transported to the transparent servers for carrying out the integration and decomposition operations. Three secure decomposition algorithms, namely secure bidiagonalization algorithm, secure singular value decomposition algorithm, and secure mode product algorithm, are presented to generate the bidiagonal matrices, truncated orthogonal bases, and core tensor respectively. The homomorphic operations of the three algorithms are carried out on the transparent servers, while the non-homomorphic operations, namely division and square root, are performed on the transparent clients. Experimental results indicate that the proposed method is promising for secure tensor decomposition for big data.

The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.

Predicting the future network traffic through big data analysis technologies has been one of the important preoccupations of network design and management. Combining Markov chains with tensors to implement predictions has received considerable attention in the era of big data. However, when dealing with multi-order Markov models, the existing approaches including the combination of states and Z-eigen decomposition still face some shortcomings. Therefore, this paper focuses on proposing a novel multivariate multi-order Markov transition to realize multi-modal accurate predictions. First, we put forward two new tensor operations including tensor join and unified product (UP). Then a general multivariate multi-order (2M) Markov model with its UP-based state transition is proposed. Afterwards, we develop a multi-step transition tensor for 2M Markov models to implement the multi-step state transition. Furthermore, an UP-based power method is proposed to calculate the stationary joint probability distribution tensor (i.e., stationary joint eigentensor, SJE) and realize SJE based multi-modal accurate predictions. Finally, a series of experiments under various Markov models on real-world network traffic datasets are conducted. Experimental results demonstrate that the proposed SJE based approach can improve the prediction accuracy for network traffic by highest up to 38.47 percentage points compared with the Z-eigen based approach.

The papers in this special section focuses on Big Data infrastructure. These papers address Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Big Data is an emerging paradigm encompassing various kinds of complex and large scale information beyond the processing capability of conventional software and databases. Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, Hadoop and Spark. Due to the multisource, massive, heterogeneous, and dynamic characteristics of application data involved in a distributed environment, one of the most important characteristics of Big Data is to carry out computing on the petabyte (PB), even the exabyte (EB)-level data with a complex computing process. Therefore, large-scale scalable Big Data Infrastructure with corresponding programming language support and software models for efficient processing in distributed environments such as cloud is on demand.

The papers in this special section focus on Big Data infrastructure. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Big Data is an emerging paradigm encompassing various kinds of complex and large scale information beyond the processing capability of conventional software and databases. Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, Hadoop and Spark. Due to the multisource, massive, heterogeneous, and dynamic characteristics of application data involved in a distributed environment, one of the most important characteristics of Big Data is to carry out computing on the petabyte (PB), even the exabyte (EB)-level data with a complex computing process. Therefore, large-scale scalable Big Data Infrastructure with corresponding programming language support and software models for efficient processing in distributed environments such as cloud is on demand. In this special issue, we invite articles on innovative research to address challenges of Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures, Hadoop or Spark with emphasis on addressing real-time requirements imposed by emerging Big Data applications such as sensing data, e-commerce data, business transactions and web logs, and etc.

With the exponential growth of data volume, big data have placed an unprecedented burden on current computing infrastructure. Dimensionality reduction of big data attracts a great deal of attention in recent years as an efficient method to extract the core data which is smaller to store and faster to process. This paper aims at addressing the three fundamental problems closely related to distributed dimensionality reduction of big data, i.e., big data fusion, dimensionality reduction algorithm and construction of distributed computing platform. A chunk tensor method is presented to fuse the unstructured, semi-structured and structured data as a unified model in which all characteristics of the heterogeneous data are appropriately arranged along the tensor orders. A Lanczos based high order singular value decomposition algorithm is proposed to reduce dimensionality of the unified model. Theoretical analyses of the algorithm are provided in terms of storage scheme, convergence property and computation cost. To execute the dimensionality reduction task, this paper employs the transparent computing paradigm to construct a distributed computing platform as well as utilizes a four-objectives optimization model to schedule the tasks. Experimental results demonstrate that the proposed holistic approach is efficient for distributed dimensionality reduction of big data.

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

Establishing promising and efficient key distribution is the cornerstone of security when applying cryptographic methods to guard the privacy of smart devices in the Internet of Things (IoT). However, when using conventional key distribution methods in real-time mobile services, it is usually deficient to build multiple channels with strong security simultaneously on a single data server. Therefore, we focus our research on collecting or monitoring real-time and remote data, which is commonly observed in the health care field of the IoT. In this research, there are difficulties to apply the conventional key distribution approaches during interactions when considering both security and efficiency. Moreover, when preserving personal privacy, anonymity is also important in key distribution processes. We design a system of real-time mobile services and the related security requirements in this paper, and apply a novel and interesting Identity-Based Key Encapsulation Mechanism (IBKEM) to instantiate our system and achieve anonymous key distribution with only one-pass communication for mobile clients in batch. Our instantiated system demonstrates both strong security and practice.

Summary Traffic hotline discovery is necessary for rational and scientific urban transportation planning in the new living quarters and economic zones. Cloud of Things (CoT) is a newly emerging concept, involving with two advanced technologies, that is, Cloud Computing and Internet of Things (IoT). CoT provides promising opportunities for traffic hotline discovery. However, it is still a challenge to discover the traffic hotlines over CoT. In view of this challenge, a hotline discovery method over CoT by using big taxi GPS data is proposed in this paper. Specifically, a traffic hotline discovery principle is presented to provide a reference standard for the generalized traffic spots that need planning, and a corresponding hotline discovery method is proposed for traffic hotspot identification and hotline selection. To improve the scalability and efficiency of the proposed method in “Big Data” environment, the SAP HANA cloud is applied to implement the proposed method under two application scenarios. Finally, the experimental results demonstrate that the proposed method is both effective and efficient. Software—Practice and Experience. Copyright © 2016 John Wiley & Sons, Ltd.

Applications in risk-critical domains such as emergency management and industrial control systems need near-real-time stream data processing in large-scale sensing networks. The key problem is how to ensure online end-to-end security (e.g., confidentiality, integrity, and authenticity) of data streams for such applications. We refer to this as an online security verification problem. Existing data security solutions cannot be applied in such applications as they cannot deal with data streams with high-volume and high-velocity data in real time. They introduce a significant buffering delay during security verification, resulting in a requirement for a large buffer size for the stream processing server. To address this problem, we propose a Dynamic Key-Length-Based Security Framework (DLSeF) based on a shared key derived from synchronized prime numbers; the key is dynamically updated at short intervals to thwart potential attacks to ensure end-to-end security. Theoretical analyses and experimental results of the DLSeF framework show that it can significantly improve the efficiency of processing stream data by reducing the security verification time and buffer usage without compromising security.

When competing in eBay bidding, online games, or e-exams in embedded computing environments, people naturally face asynchronous starts from different computing devices, which is treated as a security risk of online contests. The security risks of online contests also include eavesdropping during data transmission without intended rights, and false starts by malicious competitors, which also means asynchrony in contests. Accordingly, online contests need security guarantees, especially on synchronization. In this article, for synchronic and secure starts in a contest, we update security requirements of confidentiality, anonymity, and synchrony, comparing the current work to our previous work. Based on the updated requirements, we propose a general framework for the Advanced Secure Synchronized Reading (ASSR) system, which can hold multiple contests simultaneously in the cloud. It is important to note that the system can ignore the impacts of heterogeneity among competitors. Considering the heterogeneity both on transmission and computing, we construct a novel Randomness-reused Identity Based Key Encapsulation Mechanism (RIBKEM) to support separable decapsulation, which can shorten both decryption delay and transmission delay with the best efforts. Finally, ASSR enhances synchronization achievement for contest starts with heterogeneous delays of competitors while satisfying other security requirements. As a complement, the analysis on the provable security of ASSR is given, as well as a further analysis on the achievement of synchronization.

Big sensing data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity. Cloud computing provides a promising platform for big sensing data processing and storage as it provides a flexible stack of massive computing, storage, and software services in a scalable manner. Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big sensing data, traditional data compression techniques lack sufficient efficiency and scalability for data processing. Based on specific on-Cloud data compression requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic data units, the compression will be conducted over partitioned data chunks. To restore original data sets, some restoration functions and predictions will be designed. MapReduce is used for algorithm implementation to achieve extra scalability on Cloud. With real world meteorological big sensing data experiments on U-Cloud platform, we demonstrate that the proposed scalable compression approach based on data chunk similarity can significantly improve data compression efficiency with affordable data accuracy loss.

Driven by the growing security demands of data outsourcing applications in sustainable smart cities, encrypting clients' data has been widely accepted by academia and industry. Data encryptions should be done at the client side before outsourcing, because clouds and edges are not trusted. Therefore, how to properly encrypt data in a way that the encrypted and remotely stored data can still be queried has become a challenging issue. Though keyword searches over encrypted textual data have been extensively studied, approaches for encrypting graph-structured data with support for answering graph queries are still lacking in the literature. In this paper, we specially investigate graph encryption method for an important graph query type, called top-k Nearest Keyword (kNK) searches. We design several indexes to store necessary information for answering queries and guarantee that private information about the graph such as vertex identifiers, keywords and edges are encrypted or excluded. Security and efficiency of our graph encryption scheme are demonstrated by theoretical proofs and experiments on real-world datasets, respectively.

While cloud computing has become an attractive platform for supporting data intensive applications, a major obstacle to the adoption of cloud computing in sectors such as health and defense is the privacy risk associated with releasing datasets to third-parties in the cloud for analysis. A widely-adopted technique for data privacy preservation is to anonymize data via local recoding. However, most existing local-recoding techniques are either serial or distributed without directly optimizing scalability, thus rendering them unsuitable for big data applications. In this paper, we propose a highly scalable approach to local-recoding anonymization in cloud computing, based on Locality Sensitive Hashing (LSH). Specifically, a novel semantic distance metric is presented for use with LSH to measure the similarity between two data records. Then, LSH with the MinHash function family can be employed to divide datasets into multiple partitions for use with MapReduce to parallelize computation while preserving similarity. By using our efficient LSH-based scheme, we can anonymize each partition through the use of a recursive agglomerative $k$-member clustering algorithm. Extensive experiments on real-life datasets show that our approach significantly improves the scalability and time-efficiency of local-recoding anonymization by orders of magnitude over existing approaches.

Summary Big data is one of the most referred key words in recent information and communications technology industry. As the new‐generation distributed computing platform, cloud environments offer high efficiency and low cost for data‐intensive storage and computation for big data applications. Cloud resources and services are available in pay‐as‐you‐go mode, which brings extraordinary flexibility and cost‐effectiveness as well as minimal investments in their own computing infrastructure. However, these advantages come at a price—people no longer have direct control over their own data. Based on this view, data security becomes a major concern in the adoption of cloud computing. Authenticated key exchange is essential to a security system that is based on high‐efficiency symmetric‐key encryptions. With virtualisation technology being applied, existing key exchange schemes such as Internet key exchange become time consuming when directly deployed into cloud computing environment, especially for large‐scale tasks that involve intensive user–cloud interactions, such as scheduling and data auditing. In this paper, we propose a novel hierarchical key exchange scheme, namely hierarchical key exchange for big data in cloud, which aims at providing efficient security‐aware scheduling and auditing for cloud environments. In this novel key exchange scheme, we developed a two‐phase layer‐by‐layer iterative key exchange strategy to achieve more efficient authenticated key exchange without sacrificing the level of data security. Both theoretical analysis and experimental results demonstrate that when deployed in cloud environments with diverse server layouts, efficiency of the proposed scheme is dramatically superior to its predecessors cloud computing background key exchange and Internet key exchange schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Scientific workflows are often deployed across multiple cloud computing platforms due to their large-scale characteristic. This can be technically achieved by expanding a cloud platform. However, it is still a challenge to conduct scientific workflow executions in an energy-aware fashion across cloud platforms or even inside a cloud platform, since the cloud platform expansion will make the energy consumption a big concern. In this paper, we propose an Energy-aware Resource Allocation method, named EnReal, to address the above challenge. Basically, we leverage the dynamic deployment of virtual machines for scientific workflow executions. Specifically, an energy consumption model is presented for applications deployed across cloud computing platforms, and a corresponding energy-aware resource allocation algorithm is proposed for virtual machine scheduling to accomplish scientific workflow executions. Experimental evaluation demonstrates that the proposed method is both effective and efficient.

Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds.

The reliability and security of multimedia contents in transmission, communications, storage, and usage have attracted special attention. Robust image hashing, also referred to as perceptual image hashing, is widely applied in multimedia authentication and forensics, image retrieval, image indexing, and digital image watermarking. In this work, a novel robust image hashing method based on quaternion Zernike moments (QZMs) is proposed. QZMs offer a sound way to jointly deal with the three channels of color images without discarding chrominance information; the generated hash is thus shorter than the hash of three channels separately processing. The proposed approach's performance was evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash in length that is robust to most common image content-preserving manipulations like JPEG compression, filtering, noise, scaling, and large angle rotation operations.

With the prosperity of media streaming applications over the Internet in the past decades, multimedia data has sharply increased (categorized as multimedia big data), which exerts more pressure on the infrastructure, such as networking of the application provider. In order to move this hurdle, an increasing number of traditional media streaming applications have migrated from a private server cluster onto the cloud. With the elastic resource provisioning and centralized management of the cloud, the operational costs of media streaming application providers can decrease dramatically. However, to the best of our knowledge, existing migration solutions do not fully take viewer information such as hardware condition into consideration. In this article, we consider the deployment optimization problem named ODP by leveraging local memories at each viewer. Considering the NP-hardness of calculating the optimal solution, we turn to propose computationally tractable algorithms. Specifically, we unfold the original problem into two interactive subproblems: coarse-grained migration subproblem and fine-grained scheduling subproblem. Then, the corresponding offline approximation algorithms with performance guarantee and computational efficiency are given. The results of extensive evaluation show that compared with the baseline algorithm without leveraging local memories at viewers, our proposed algorithms and their online versions can decrease total bandwidth reservation and enhance the utilization of bandwidth reservation dramatically.

Summary To assess the quality of services (QoS) in service selection, collaborative service QoS prediction has recently garnered increasing attention. They focus on exploring the historical QoS information generated by interactions between users and services. However, they may suffer from the data sparsity issue because interactions between users and services are usually sparse in real scenarios. They also seldom consider the network environments of users and services, which surely will affect cloud service QoS. To address the data sparsity issue and improve the QoS prediction accuracy, the following paper proposes a collaborative QoS prediction method with location‐based data smoothing. The method first computes neighborhoods of users and services based on their locations which provide a basis for data smoothing. It then combines user‐based and service‐based collaborative filtering techniques to make QoS predictions. Experiments conducted using a real service invocation dataset validate the performance of the proposed QoS prediction method. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Traditionally, a service is often located by its functional specification, and then evaluated by its QoS properties. In practice, a service may have multiple functional specifications for satisfying different users with different expectation descriptions around a similar functional property. For example, a song retrieval service may be enacted by the name of a song or enacted by the lyric of the song if a user does not know the name of the song. In view of this challenge, a service evaluation method promoted by multi‐functional specification is proposed for cross‐cloud service choreography, in this paper. Concretely speaking, a mixed integer programming model is employed to decompose global constraints into local constraints; then, the evaluation is promoted by multi‐functional specification and enacted by brokers for cross‐cloud service choreography. At last, experiments are presented to verify our method. Copyright © 2013 John Wiley & Sons, Ltd.

Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users' direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.

With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and virtual machine (VM ) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multi-criteria-based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius’ applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA.

Cloud computing provides promising scalable IT infrastructure to support various processing of a variety of big data applications in sectors such as healthcare and business. Data sets like electronic health records in such applications often contain privacy-sensitive information, which brings about privacy concerns potentially if the information is released or shared to third-parties in cloud. A practical and widely-adopted technique for data privacy preservation is to anonymize data via generalization to satisfy a given privacy model. However, most existing privacy preserving approaches tailored to small-scale data sets often fall short when encountering big data, due to their insufficiency or poor scalability. In this paper, we investigate the local-recoding problem for big data anonymization against proximity privacy breaches and attempt to identify a scalable solution to this problem. Specifically, we present a proximity privacy model with allowing semantic proximity of sensitive values and multiple sensitive attributes, and model the problem of local recoding as a proximity-aware clustering problem. A scalable two-phase clustering approach consisting of a t-ancestors clustering (similar to k-means) algorithm and a proximity-aware agglomerative clustering algorithm is proposed to address the above problem. We design the algorithms with MapReduce to gain high scalability by performing data-parallel computation in cloud. Extensive experiments on real-life data sets demonstrate that our approach significantly improves the capability of defending the proximity privacy breaches, the scalability and the time-efficiency of local-recoding anonymization over existing approaches.

Cloud computing promises a scalable infrastructure for processing big data applications such as medical data analysis. Cross-cloud service composition provides a concrete approach capable for large-scale big data processing. However, the complexity of potential compositions of cloud services calls for new composition and aggregation methods, especially when some private clouds refuse to disclose all details of their service transaction records due to business privacy concerns in cross-cloud scenarios. Moreover, the credibility of cross-clouds and on-line service compositions will become suspicional, if a cloud fails to deliver its services according to its “promised” quality. In view of these challenges, we propose a privacy-aware cross-cloud service composition method, named HireSome-II (History record-based Service optimization method) based on its previous basic version HireSome-I. In our method, to enhance the credibility of a composition plan, the evaluation of a service is promoted by some of its QoS history records, rather than its advertised QoS values. Besides, the k-means algorithm is introduced into our method as a data filtering tool to select representative history records. As a result, HireSome-II can protect cloud privacy, as a cloud is not required to unveil all its transaction records. Furthermore, it significantly reduces the time complexity of developing a cross-cloud service composition plan as only representative ones are recruited, which is demanded for big data processing. Simulation and analytical results demonstrate the validity of our method compared to a benchmark.

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

SUMMARY Resource performance prediction is becoming more and more important in cloud environment, and CPU load prediction is helpful for system maintenance and application schedule. However, the best predictor often varies from one resource to another. At the same time, CPU load in cloud environment has a wide range of dynamics so that the best predictor for any particular CPU load time series may change over time. Ensemble method, which can use multiple models to obtain better performances, is investigated to support CPU load prediction in this paper. The novel ensemble model proposed consists of two layers. The predictor optimization layer can continuously incorporate new predictor instances and remove those ones with a poor performance. The ensemble layer is responsible for producing the final prediction based on the results of multiple predictor instances. In addition, the ensemble layer can also provide feedbacks to the predictor optimization layer, which helps it to adopt appropriate optimization strategies. Extensive experiments have been performed on CPU load traces collected from an in‐production private cloud environment, and it shows this ensemble model has a better performance than any individual model. Copyright © 2013 John Wiley & Sons, Ltd.

Scientific processes are usually time constrained with overall deadlines and local milestones. In scientific workflow systems, due to the dynamic nature of the underlying computing infrastructures such as grid and cloud, execution delays often take place and result in a large number of temporal violations. Since temporal violation handling is expensive in terms of both monetary costs and time overheads, an essential question aroused is “do we need to handle every temporal violation in scientific workflow systems?” The answer would be “true” according to existing works on workflow temporal management which adopt the philosophy similar to the handling of functional exceptions, that is, every temporal violation should be handled whenever it is detected. However, based on our observation, the phenomenon of self-recovery where execution delays can be automatically compensated for by the saved execution time of subsequent workflow activities has been entirely overlooked. Therefore, considering the nonfunctional nature of temporal violations, our answer is “not necessarily true.” To take advantage of self-recovery, this article proposes a novel adaptive temporal violation handling point selection strategy where this phenomenon is effectively utilised to avoid unnecessary temporal violation handling. Based on simulations of both real-world scientific workflows and randomly generated test cases, the experimental results demonstrate that our strategy can significantly reduce the cost on temporal violation handling by over 96% while maintaining extreme low violation rate under normal circumstances.

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-a-service’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.

Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly improves the accuracy and scalability of service recommender systems over existing approaches.

A large number of cloud services require users to share private data like electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.

Community Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely$MC^{3}$. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in$MC^{3}$. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds.

Web service composition lets users create value-added composite Web services on existent services, where top-k composite services are helpful for users to find a satisfying composite service efficiently. However, with an increasing number of Web services and users' various composition preferences, computing top-k composite services dynamically for different users is difficult. In view of this challenge, a top-k composite services selection method is proposed, based on a preference-aware service dominance relationship. Concretely speaking, firstly, user preferences are modeled with the preference-aware service dominance, and then, in local service selection, a multi-index based algorithm is proposed, named Multi-Index, for computing candidate services of each task dynamically. Then, in global optimization, combined with a service lattice, top-k composite services are selected under a dominant number-aware service ranking. At last, an experiment is presented to verify our method.

This special issue of Concurrency and Computation: Practice and Experience contains selected highquality papers from the 2011 International Conference on Cloud and Green Computing (CGC2011) which was held on December 11-13, 2011 in Sydney, Australia [1].The CGC conference series aims to provide an international forum for the presentation and discussion of research and development trends regarding cloud and green computing.CGC2011 attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants.Many research and development efforts have been made in the field of cloud and green computing such as [2][3][4][5][6][7][8][9][10][11].More and more people from different areas are trying to facilitate the techniques from their respective areas to tackle tough issues in cloud and green computing such as resource scheduling, security and privacy, service provision, power aware computation and storage, and data service query issues.This special issue aims to accommodate a range of papers from different perspectives and areas to provide some different views and hints for cloud and green computing research.This special issue contains eight papers based on those that were presented at CGC2011.They are listed as [12][13][14][15][16][17][18][19].Research problems in these papers have been analyzed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages.The papers were selected on this basis and also peer reviewed thoroughly.They are summarized in the succeeding texts.Paper [12] develops an adaptive service selection method for cross-cloud service composition.It can dynamically select proper services with near-optimal performance for adapting to changes in time.A case study is presented to demonstrate the performance.Paper [13] attempts to identify the role of contextual properties of enterprise systems architecture in relation to service migration to cloud computing.It points out that cloud computing requires consumers to relinquish their ownership of and control over most architectural elements to cloud providers.The simulation is conducted to evaluate the feasibility of the proposed method.Paper [14] proposes an economic and energy aware cloud cost model in this regard.The model supports the decision-making process to be applied with business cases and enables cloud consumers and cloud providers to define their own business strategies and to analyze the respective impact on their business.Paper [15] focuses on latency in global cloud service provision.This paper investigates if latency in terms of simple ping measurements can be used as an indicator for other QoS parameters such as jitter and throughput.Corresponding experiments are conducted to demonstrate performance.Paper [16] presents a number of policies that can be applied to multiuse clusters where computers are shared between interactive users and high throughput computing.The paper also evaluates policies by trace-driven simulations to determine the effects on power consumed by the high throughput workload and impact on high throughput users.The experiment results demonstrate significant power saving with proposed policies.Paper [17] designs an efficient data and task co-scheduling strategy for scheduling datasets and tasks together.Simulation was conducted on the well-known Tianhe supercomputer platform.Simulation results demonstrate that the proposed strategy can effectively improve workflows performance while reducing the total volume of data transfer across data centers.

This special issue of Concurrency and Computation: Practice and Experience contains selected high-quality papers from the Second International Workshop on Workflow Management in Service and Cloud Computing (WMSC2010) that was held on 11–13 December 2010 in Hong Kong 1. The WMSC workshop series aims to provide an international forum for the presentation and discussion of research and development trends regarding workflow support in service and cloud environments. WMSC2010 attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants. Following ICWM2009 on 4 May 4 2009 in Geneva, Switzerland, WaGe2008 on 25 May 2008 in Kunming China and WaGe2007 on 17 August 2007 in Urumqi China; WMSC2010 continues to discuss workflow management in service and cloud environments from different perspectives and areas in order to tackle different potentials for further research and development. Workflow management in distributed computing environments has been under investigation for several years 2-8. In particular, the special issue titled Workflow in Grid Systems in Concurrency and Computation: Practice and Experience was a key step 7. The special issue was edited by Professor Geoffrey C. Fox and Professor Dennis Gannon from Indiana University in USA. A follow-up were the special issues in the same journal for WSGE2006 (first International Workshop on Workflow Systems in Grid Environments), WaGe2007, WaGe2008 and ICWM2009 9-12. This WMSC2010 special issue is another follow-up of those special issues in order to further boost the research and development of workflow management and applications. Many research and development efforts have been made in the field of workflow management and applications in distributed service and cloud environments such as 2-8, 13-21. More and more people from different areas are trying to facilitate the techniques from their respective areas to tackle tough issues in workflow management such as resource scheduling, security, computation reduction, service discovery and composition and data service query issues. Following the special issue of ICWM2009, this special issue continues to accommodate a range of papers from different perspectives and areas such as service computing, cloud computing, authentication/security in order to provide some different views and hints for workflow management research. This special issue contains nine papers based on those that were presented at WMSC2010. They are listed as 22-30. Research problems in these papers have been analysed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages. The nine papers were selected on this basis and also peer reviewed thoroughly. They are summarised in the succeeding text. This paper 22 is a workflow scheduling in dynamic grid environment. The paper defines the workflow scheduling problem and describes the existing heuristic and meta-heuristic-based workflow scheduling strategies in Grids. Then, we propose a dynamic critical path-based adaptive workflow scheduling algorithm for Grids, which determines efficient mapping of workflow tasks to Grid resources dynamically by calculating the critical path in the workflow task graph at every step. Corresponding evaluation is conducted to demonstrate the performance. This paper 23 is about service discovery in elastic cloud computing environment. A QoS-aware service discovery method is investigated for elastic cloud computing in an unstructured P2P network. The method is deployed by two phases, that is, service registering phase and service discovery phase. More specifically, for a peer node engaged in the unstructured P2P network, it firstly registers its functional and non-functional information to its neighbours in a flooding way. The simulations are conducted to evaluate the feasibility of our method. This paper 24 is about service-oriented business ecosystem (SOBE). It presents BSNet, a model based on the service correlation networks to manage the SOBE. The model consists of a who–what–how service correlation network that captures the various relations in SOBE. Finally, a prototyping system has been developed to demonstrate the value of this model for business service management and a simulation-based case study is also provided. This paper 25 proposes the concept of graph refactoring, which transforms certain types of sequential tasks to run in parallel without changing the system's functionality. Experiments and analysis show that graph refactoring can improve the system performance scalable because of concurrent execution of previously sequential tasks. This paper 26 proposes a novel Virtual Organization (VO) creating algorithm (called Group-Choose) based on reputation system that can help initiator to minimise the operating risk and guarantee the success on Internet. In our model, a VO initiator aggregate selected partner's trust to find more appropriate new partners for VO, instead of evaluating their trust by himself only. Simulation results illustrate that VO creating with Group-Choose has more stable and higher success rates for tasks workflow executing under various kind attacks. This paper 27 proposes a service correlation context aware for composite service selection approach is proposed in this paper. Referring to the concept of ‘single-entry single-exit (SESE) region’ in compiler theory, the paper proposes the concept of ‘SESE pattern’ and uses it in composite service selection. Experimental results demonstrate that the approach can improve the quality of selected composite services effectively in the correlation context. This paper 28 proposes an approach to mine batch processing workflow models from event logs by considering the batch processing relations among activity instances in multiple workflow cases. The notion of batch processing feature and its corresponding mining algorithm are also presented for discovering the batch processing area in the model by using the input and output data information of activity instances in events. The algorithms presented in this paper can help to enhance the applicability of existing process mining approaches and broaden the process mining spectrum. This paper 29 proposes an event view model in order to better support service process collaboration. The model is composed of a set of event types and their dependency relationships. It provides a general and flexible way to define a public view of a service process model and serves as the basis for defining service process collaboration protocols. A case study is presented and some implementation issues for defining and publishing an event view are discussed. This paper 30 is about encrypted database query in service cloud environment. The paper proposes a nonlinear order-preserving scheme for indexing encrypted data, which facilitates the range queries over encrypted databases. The scheme is secure even there are a large number of duplicates in plaintexts. This scheme is suitable for long-standing databases because its use does not need any assumption on the database data such as their distribution, range and number, which may change dramatically over time.

SUMMARY Due to its advantages of cost‐effectiveness, on‐demand provisioning and easy for sharing, cloud computing has grown in popularity with the research community for deploying scientific applications such as workflows. Although such interests continue growing and scientific workflows are widely deployed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place application datasets across globally distributed data centers and schedule tasks according to the data layout to reduce both latency and makespan for workflow execution. In this paper, by utilizing dependencies among datasets and tasks, we propose an efficient data and task coscheduling strategy that can place input datasets in a load balance way and meanwhile, group the mostly related datasets and tasks together. Moreover, data staging is used to overlap task execution with data transmission in order to shorten the start time of tasks. We build a simulation environment on Tianhe supercomputer for evaluating the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve scheduling performance while reducing the total volume of data transfer across data centers. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

SUMMARY Traditionally, service discovery is often promoted by the centralized approach that typically suffers from single point of failure, poor reliability, poor scalability, to name a few. In view of this challenge, a QoS‐aware service discovery method is investigated for elastic cloud computing in an unstructured peer‐to‐peer network in this paper. Concretely speaking, the method is deployed by two phases, that is, service registering phase and service discovery phase. More specifically, for a peer node engaged in the unstructured peer‐to‐peer network, it firstly registers its functional and nonfunctional information to its neighbors in a flooding way. With the multiple registered information, the QoS‐aware service discovery is promoted in a probabilistic flooding way according to the network traffic. At last, extensive simulations are conducted to evaluate the feasibility of our method. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY The employment of batch processing in workflow is to model and schedule activity instances in multiple workflow cases of the same workflow type to optimize business processes execution dynamically. Although our previous works have preliminarily investigated its model and implementation, it is still necessary to deal with its model design problem. Process mining techniques allow for the automated discovery of process models from event logs and have received notable attentions in researches recently. Following these researches, this paper proposes an approach to mine batch processing workflow models from event logs by considering the batch processing relations among activity instances in multiple workflow cases. The notion of batch processing feature and its corresponding mining algorithm are also presented for discovering the batch processing area in the model by using the input and output data information of activity instances in events. The algorithms presented in this paper can help to enhance the applicability of existing process mining approaches and broaden the process mining spectrum. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to the current information technology industry and research communities while posing significant challenges on them as well. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever increasing data sets in big data applications. However, processing or sharing privacy‐sensitive data sets on cloud probably engenders severe privacy concerns because of multi‐tenancy. Data encryption and anonymization are two widely‐adopted ways to combat privacy breach. However, encryption is not suitable for data that are processed and shared frequently, and anonymizing big data and manage numerous anonymized data sets are still challenges for traditional anonymization approaches. As such, we propose a scalable and cost‐effective framework for privacy preservation over big data on cloud in this paper. The key idea of the framework is that it leverages cloud‐based MapReduce to conduct data anonymization and manage anonymous data sets, before releasing data to others. The framework provides a holistic conceptual foundation for privacy preservation over big data. Further, a corresponding proof‐of‐concept prototype system is implemented. Empirical evaluations demonstrate that scalable and cost‐effective framework for privacy preservation can anonymize large‐scale data sets and mange anonymous data sets in a highly flexible, scalable, efficient, and cost‐effective fashion. Copyright © 2013 John Wiley & Sons, Ltd.

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.

In current Cloud computing environments, management of data reliability has become a challenge. For data-intensive scientific applications, storing data in the Cloud with the typical 3-replica replication strategy for managing the data reliability would incur huge storage cost. To address this issue, in this paper we present a novel cost-effective data reliability management mechanism named PRCR, which proactively checks the availability of replicas for maintaining data reliability. Our simulation indicates that, comparing with the typical 3-replica replication strategy, PRCR can reduce the storage space consumption by one-third to two-thirds, hence reduce the storage cost significantly in the Cloud.

Cloud computing promises an open environment where customers can deploy IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers may exist. Such service providers may record service information in a service process from a customer and then collectively deduce the customer's private information. Therefore, from the perspective of cloud computing security, there is a need to take special actions to protect privacy at client sides. Noise obfuscation is an effective approach in this regard by utilising noise data. For instance, it generates and injects noise service requests into real customer service requests so that service providers would not be able to distinguish which requests are real ones if their occurrence probabilities are about the same. However, existing typical noise generation strategies mainly focus on the entire service usage period to achieve about the same final occurrence probabilities of service requests. In fact, such probabilities can fluctuate in a time interval such as three months and may significantly differ than other time intervals. In this case, service providers may still be able to deduce the customers' privacy from a specific time interval although unlikely from the overall period. That is to say, the existing typical noise generation strategies could fail to protect customers' privacy for local time intervals. To address this problem, we develop a novel time-series pattern based noise generation strategy. Firstly, we analyse previous probability fluctuations and propose a group of time-series patterns for predicting future fluctuated probabilities. Then, based on these patterns, we present our strategy by forecasting future occurrence probabilities of real service requests and generating noise requests to reach about the same final probabilities in the next time interval. The simulation evaluation demonstrates that our strategy can cope with these fluctuations to significantly improve the effectiveness of customers' privacy protection.

SUMMARY Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science in the cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay‐for‐use model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenance in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such we develop a novel intermediate data storage strategy that can reduce the cost of scientific cloud workflow systems by automatically storing appropriate intermediate data sets with one cloud service provider. The strategy has significant research merits, i.e. it achieves a cost‐effective trade‐off of computation cost and storage cost and is not strongly impacted by the forecasting inaccuracy of data sets' usages. Meanwhile, the strategy also takes the users' tolerance of data accessing delay into consideration. We utilize Amazon's cost model and apply the strategy to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly. Copyright © 2010 John Wiley & Sons, Ltd.

SUMMARY Cloud promises users that they can present and deploy IT services in a pay‐as‐you‐go fashion in an open and virtualized cloud environment while saving huge capital investment in their own IT infrastructure. In this sense, protection of users' privacy is critical and has become one of the most concerned issues as otherwise users may eventually lose the confidence and passion of deploying cloud in practice. Under some special cloud circumstances, some users' privacy, such as plans or habits, could be induced from their service requests by service providers without permissions from users. In this regard, obfuscation strategy can protect this kind of privacy by injecting ‘noise’ service requests to confuse potential ‘immoral’ service providers. However, existing noise obfuscation strategies focus on single noise injection whereas investigation of noise injection architecture has been neglected. Especially, a common service pattern in inter‐clouds environment, the cooperative service process including different service providers, makes the risk of privacy serious and uncontrollable by the spread of users' privacy. To address this, we present a novel trust‐based noise injection strategy for privacy protection in cloud. To support the strategy, we describe our noise injection architecture in cloud which specializes in the relations between various service roles in inter‐clouds based on our trust model. The simulation can demonstrate that our noise injection strategy could significantly improve the effectiveness of privacy protection. Copyright © 2011 John Wiley & Sons, Ltd.

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application datasets can be stored in the cloud. However, due to the pay-as-you-go model, the datasets should be strategically stored in order to reduce the overall application cost. In this paper, by utilising Data Dependency Graph (DDG) from data provenances in scientific applications, deleted datasets can be regenerated, and as such we develop a novel cost-effective datasets storage strategy that can automatically store appropriate datasets in the cloud. This strategy achieves a localised optimal trade-off between computation and storage, meanwhile also taking users' tolerance of data accessing delay into consideration. Simulations conducted on general (random) datasets and a specific astrophysics pulsar searching application with Amazon's cost model show that our strategy can reduce the application cost significantly.

Instead of purchasing and maintaining their own computing infrastructure, scientists can now run data-intensive scientific applications in cloud computing environment by facilitating its vast storage and computation capabilities. During the scheduling of such scientific applications for execution, various computation data flows will happen between the controller and computing server instances. Amongst various quality-of-service (QoS) metrics, data security is one of the greatest concerns to scientists because their data may be intercepted or stolen by malicious parties during those data flows. An existing typical method for addressing this issue is to apply Internet Key Exchange (IKE) scheme to generate and exchange session keys, and then to apply these keys for performing symmetric-key encryption which will encrypt those data flows. However, the IKE scheme suffers from low efficiency due to its low performance of asymmetric-key crypto logical operations over a large amount of data and high-density operations which are exactly the characteristics of scientific applications. In this paper, we propose Cloud Computing Background Key Exchange (CCBKE), a novel authenticated key exchange scheme that aims at efficient security-aware scheduling of scientific applications. Our scheme is designed based on randomness-reuse strategy and Internet Key Exchange (IKE) scheme. Theoretical analyses and simulation results demonstrate that, compared with the IKE scheme, our CCBKE scheme can significantly improve the efficiency by dramatically reducing time consumption and computation load without sacrificing the level of security.

Along with more and more data intensive applications have been migrated into cloud environments, storing some valuable intermediate datasets has been accommodated in order to avoid the high cost of re-computing them. However, this poses a risk on data privacy protection because malicious parties may deduce the private information of the parent dataset or original dataset by analyzing some of those stored intermediate datasets. The traditional way for addressing this issue is to encrypt all of those stored datasets so that they can be hidden. We argue that this is neither efficient nor cost-effective because it is not necessary to encrypt ALL of those datasets and encryption of all large amounts of datasets can be very costly. In this paper, we propose a new approach to identify which stored datasets need to be encrypted and which not. Through intensive analysis of information theory, our approach designs an upper bound on privacy measure. As long as the overall mixed information amount of some stored datasets is no more than that upper bound, those datasets do not need to be encrypted while privacy can still be protected. A tree model is leveraged to analyze privacy disclosure of datasets, and privacy requirements are decomposed and satisfied layer by layer. With a heuristic implementation of this approach, evaluation results demonstrate that the cost for encrypting intermediate datasets decreases significantly compared with the traditional approach while the privacy protection of parent or original dataset is guaranteed.

In service selection, an end user often has his or her personal preferences imposing on a candidate service's non-functional properties. For a service selection process promoted by a group of users, candidate services are often evaluated by a group of end users who may have different preferences or priorities. In this situation, it is often a challenging effort to make a tradeoff among various preferences or priorities of the users. In view of this challenge, a multi-criteria decision-making method, named AHP (Analytic Hierarchy Process), is introduced to transform both qualitative personal preferences and users' priorities into numeric weights. Furthermore, a QoS-aware service evaluation method is presented for a shared service's co-selection taking advantage of AHP theory. At last, a case study is presented to demonstrate the feasibility of the method.

The performance of a service's QoS may evolve relatively frequently with its internal changes or the changes of dynamic Internet environment, especially when some"intentional" deceptions are taken into consideration. Therefore, the service providers could not always deliver their services according to their "promised" quality. In view of this challenge, a history record-based service optimization method, named Hire Some, is investigated in this paper. This method aims at enhancing the credibility of service composition plan, taking advantage of a web service's QoShistory records, rather than using the tentative QoS values advertised by the service provider. At last, a case study and an experiment is presented for validating the method.

Introduction to a special issue of Concurrency and Computation: Practice and Experience containing selected papers from the Fourth International Workshop on Workflow Management (ICWM2009), which was held on 04 May 2009 in Geneva, Switzerland.

Abstract In scientific workflow systems, it is critical to ensure the timely completion of scientific workflows. Therefore, temporal constraints as a type of QoS (Quality of Service) specification are usually required to be managed in scientific workflow systems. Specifically, temporal constraint management includes two basic tasks: setting temporal constraints at workflow build‐time and updating temporal constraints at workflow run‐time. For constraint setting, the current work mainly adopts user‐specified temporal constraints without considering the system performance. Hence, it may result in frequent temporal violations which deteriorate the overall workflow execution effectiveness. As regards constraint updating, although not well investigated, so far is in fact of great importance to workflow management tasks such as workflow scheduling and exception handling. In this paper, with a systematic analysis of the above issues, we propose a probabilistic strategy for temporal constraint management which utilizes a novel probability‐based temporal consistency model. Specifically for constraint setting, a negotiation process between the client and the service provider is designed to support the setting of coarse‐grained temporal constraints and then automatically derive the fine‐grained temporal constraints; for constraint updating, the probability time deficit/redundancy propagation process is proposed to update run‐time fine‐grained temporal constraints when workflow execution is either ahead of or behind the schedule. The effectiveness of our strategy is demonstrated through a case study on an example scientific workflow process in our scientific workflow system. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Scientific workflow is gaining ever‐increasing attention as it integrates a wide range of heterogeneous services to enable and accelerate the scientific discovery processes. However, the exception occurred in a scientific workflow execution often decreases the robustness of a scientific workflow system. For example, if a service engaged in a scientific workflow schedule becomes unavailable during its execution, an exception might be raised and the whole workflow execution may be interrupted unexpectedly. In this situation, it is a challenge to smooth the interrupted workflow execution. In view of this challenge, an exception handling method, named RelaxingMe (constraints Relaxing Method, RelaxingMe) is proposed in this paper. This method aims at relaxing the original QoS constraint values requested by the interrupted task node, in order to find a near‐to‐optimal candidate service to replace the unavailable one. At last, the feasibility of our method is evaluated by an experiment. Copyright © 2011 John Wiley & Sons, Ltd.

In a scientific workflow system, a checkpoint selection strategy is used to select checkpoints along scientific workflow execution for verifying temporal constraints so that we can identify any temporal violations and handle them in time in order to ensure overall temporal correctness of the execution that is often essential for the usefulness of execution results. The problem of existing representative strategies is that they do not differentiate temporal constraints as, once a checkpoint is selected, they verify all temporal constraints. However, such a checkpoint does not need to be taken for those constraints whose consistency can be deduced from others. The corresponding verification of such constraints is consequently unnecessary and can severely impact overall temporal verification efficiency while the efficiency determines whether temporal violations can be identified quickly for handling in time. To address the problem, in this article, we develop a new temporal-dependency based checkpoint selection strategy which can select checkpoints in accordance with different temporal constraints. With our strategy, the corresponding unnecessary verification can be avoided. The comparison and experimental simulation further demonstrate that our new strategy can improve the efficiency of overall temporal verification significantly over the existing representative strategies.

Although semantic-based composition approaches have brought some comprehensive advantages such as higher precisions and recalls, they are far from the real practice and hard to be applied in real-world applications due to the several challenging issues such as performance issues of time-consuming ontology reasoning, exponentially expanded searching time in large service repositories, lack of available and consensus ontologies, and higher using thresholds for users who do not have much semantic knowledge. To reduce these issues, in this paper, we present an innovative composition technique by building an Extended Quick Service Query List (EQSQL) for supporting more efficient and more realistic service composition. In EQSQL, data structures are specially designed to record service information and their associated semantic concepts by in advance processing semantic-related computing during service publication period. Particularly, WordNet and semantic similarities among multiple heterogeneous ontologies are exploited in our developed algorithms for forming EQSQL. As a result, EQSQL-based planning algorithm can not only achieve a quick response for a composition request, but guarantee the semantic composition quality as well. More importantly, our approaches can be scalable to the large service repositories and also significantly alleviate users or developers from the burden of using complicated semantic service composition, thus making service composition easier and more realistic. Our final experiments further demonstrate the feasibility and the efficiency of our proposed approaches.

Due to the dynamic nature of the underlying high-performance infrastructures for scientific workflows such as grid and cloud computing, failures of timely completion of important scientific activities, namely, temporal violations, often take place. Unlike conventional exception handling on functional failures, nonfunctional QoS failures such as temporal violations cannot be passively recovered. They need to be proactively prevented through dynamically monitoring and adjusting the temporal consistency states of scientific workflows at runtime. However, current research on workflow temporal verification mainly focuses on runtime monitoring, while the adjusting strategy for temporal consistency states, namely, temporal adjustment, has so far not been thoroughly investigated. For this issue, two fundamental problems of temporal adjustment, namely, where and how, are systematically analyzed and addressed in this paper. Specifically, a novel minimum probability time redundancy-based necessary and sufficient adjustment point selection strategy is proposed to address the problem of where and an innovative genetic-algorithm-based effective and efficient local rescheduling strategy is proposed to tackle the problem of how. The results of large-scale simulation experiments with generic workflows and specific real-world applications demonstrate that our temporal adjustment strategy can remarkably prevent the violations of both local and global temporal constraints in scientific workflows.

Due to the complex nature of scientific workflow systems, the violations of temporal QoS constraints often take place and may severely affect the usefulness of the execution's results. Therefore, to deliver satisfactory QoS, temporal violations need to be recovered effectively. However, there are two fundamental issues which have so far not been well addressed: how to define recoverable temporal violations, how to design corresponding exception handling strategies. In this paper, we first propose a probability based temporal consistency model to define the temporal violations which are statistically recoverable by light-weight exception handling strategies. Afterwards, a novel Ant Colony Optimisation based two-stage workflow local rescheduling strategy (ACOWR) is proposed to handle detected recoverable temporal violations in an automatic and cost-effective fashion. The simulation experiments conducted in our scientific workflow system demonstrate the excellent performance of our handling strategy in reducing both local and global temporal violation rates.

There exist so many web services that share same or similar functional properties, so it is often a challenging effort to select a credible and optimal web service based on their various history QoS records. In view of this challenge, in this paper, a novel QoS-aware web service selection method is put forward, based on credibility evaluation associated with negotiated QoS dimensions. More specifically, the historical empirical data, i.e., execution logs of a service, are used for evaluation purpose. At last, a case study is employed for illustration purpose, and the evaluations are presented to demonstrate the feasibility of our method.

Temporal violations may often take place and deteriorate the overall QoS of scientific workflows. To handle temporal violations in an automatic and cost-effective fashion, we need to resolve the following issues: 1) how to define fine-grained recoverable temporal violations, 2) which light-weight effective exception handling strategies to be facilitated. This paper proposes an effective exception handling framework. Based on a probability based temporal consistency model, the probability range for recoverable temporal violations is divided into three levels of fine-grained temporal violations. Afterwards, three corresponding light-weight exception handling strategies including TDA (Time Deficit Allocation), ACOWR (Ant Colony Optimisation based two-stage Workflow local Rescheduling) and TDA+ACOWR (the combined strategy of TDA and ACOWR) are presented. The experimental results demonstrate the excellent performance of our framework in reducing both local and global temporal violations.

Web service composition, i.e., WSC, has emerged as a promising way to integrate various distributed computing resources for complex application requirements. However, much computation time is needed to determine the optimal composite solution, which embarrasses the popularity of WSC in actual real time applications. In view of this challenge, in this paper, a heuristic service composition method, named LOEM (Local Optimization and Enumeration Method, LOEM), is proposed. It aims at filtering the candidates of each task to a small number of promising ones by local selection, and then enumerates all the composite solutions to pursue a near-to-optimal one. The experiment results demonstrate the feasibility of LOEM in dealing with the WSC problems.

Many scientific workflows are data intensive where a large volume of intermediate data is generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay-for-use model. In this paper, we build an Intermediate data Dependency Graph (IDG) from the data provenances in scientific workflows. Based on the IDG, we develop a novel intermediate data storage strategy that can reduce the cost of the scientific cloud workflow system by automatically storing the most appropriate intermediate datasets in the cloud storage. We utilise Amazon's cost model and apply the strategy to an astrophysics pulsar searching scientific workflow for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly.

A critical issue in service composition area is how to achieve an optimized overall end-to-end quality of service(QoS) requirements by effectively coordinating QoS constraints for individual service. However, this issue has not yet been well addressed. In this paper, we propose a novel method by employing a recursive bargaining Strategy to gradually remove QoS constraint violations for Optimizing service composition execution Path. Our method mainly exploits the hidden market competitive relationships which widely exist in real business world for developing a novel bargaining strategy. Based on this strategy, concessions can be made by service providers to offer better QoS values. By recursively using bargaining strategy, an initial execution path built by a local optimization policy for service composition, can be continually updated to be close to the optimal one by reselecting better service providers for meeting overall end-to-end QoS requirements. An experiment and evaluation have been made to demonstrate the feasibility and effectiveness of our proposed method.

The instance-aspect oriented workflow management system is to vertically combine multiple workflow activity instances and submit them for execution as a whole according to some batch or combination logics. It is inspired by the idea of aspect-oriented programming methodology and aims at improving the execution efficiency of business processes. Traditional workflow systems do not support workflow model with instance aspects. In our previous work, we have studied workflow instance modeling technology. This paper makes a research on the principles, methods and implementation of a workflow visual GUI tool for modeling instance aspects in workflow. It is based on an open source GUI tool, Together Workflow Editor, and makes some expansion in instance aspect functionality.

Workshop on Workflow Management and Applications in Grid Environments (

Abstract A service‐based scientific workflow can be exposed as a composite service that consists of a set of logically connected sub‐services. A critical issue in this area is how to achieve overall optimized end‐to‐end QoS requirements by effectively coordinating individual QoS constraints of single service. Unfortunately, this issue has not been well addressed. In this paper, we propose a Reverse Order‐based approach to gradually remove QoS Constraint violations for building an optimized path to execute a scientific workflow. With our approach, an initial execution path for a scientific workflow is first built by employing the local optimization policy without considering user‐defined end‐to‐end QoS constraints. Based on this path, global QoS computing models can be used to calculate the global QoS values for each quality attribute. Then, QoS constraint violations can be detected by comparing global QoS values with end‐to‐end user‐defined QoS constraints. For each violation, a Reverse Order‐based correction algorithm by gradually removing QoS constraint violations is proposed to recursively correct it by reselecting critical service execution instances. As a result, an optimized execution path can be rebuilt to meet overall end‐to‐end QoS requirements. Comparison and simulation further demonstrate the feasibility and performance of our approach. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Scientific workflow is emerging as a promising scientific computing paradigm to offer the convenience for the scientists to resolve complex scientific problems. To successfully execute a scientific workflow, the workflow creation by depending on service discovery techniques should be made in the first place. Particularly, semantics have been proposed as a key to automatically solve service discovery issue for facilitating users to create a workflow. However, most of the semantic service discovery methods still remain at a low‐efficiency stage because they generally involve a large number of ontology reasoning that is often time consuming. To address this issue, we present an efficient service discovery method by building Quick Service Query list (QSQL) to support automated service discovery for creating a workflow. QSQL based on graph storage theory is an efficient service index list that is dynamically built by service publication algorithm. In QSQL, semantic relationships between the published services and all related ontology concepts can be processed in advance so that a large number of ontology reasoning can be avoided during service discovery. Further, our proposed discovery algorithm can efficiently select service models from QSQL to match a user query. The final experiments further demonstrate the feasibility and the efficiency of our proposed method. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Robustness and reliability with respect to the successful completion of a schedule are crucial requirements for scheduling in scientific workflow management systems because service providers are becoming autonomous. We introduce a model to incorporate trust, which indicates the probability that a service agent will comply with its commitments to improve the predictability and stability of the schedule. To deal with exceptions during the execution of a schedule, we adapt and evolve the schedule at runtime by interleaving the processes of evaluating, scheduling, executing and monitoring in the life cycle of the workflow management. Experiments show that schedules maximizing participants' trust are more likely to survive and succeed in open and dynamic environments. The results also prove that the proposed approach of workflow evaluation can find the most robust execution flow efficiently, thus avoiding the need of scheduling every possible execution path in the workflow definition. Copyright © 2009 John Wiley & Sons, Ltd.

Current existing semantic composition methods mainly rely on ontology reasoning to support automated service composition. However, in reality, ontologies are generally unavailable or ontology reasoning is time-consuming; thus existing semantic composition methods are becoming impractical in the general service integration field. To address this problem, in this paper, we present an innovative composition technique by combining Wordnet with ontologies together to build an extended quick service query list (EQSQL) for supporting automated service composition. In EQSQL, data structures are designed particularly to record service information and their associated semantic concepts by previously processing semantic-related computing during service publication stage. Based on EQSQL, not only a quick response can be achieved, but the semantically-similar composition quality as well even if there is a lack of concrete domain-dependent ontologies for a user query.

In scientific workflow systems, time related functionalities such as workflow scheduling and temporal verification normally require effective forecasting of activity durations due to the dynamic nature of underlying resources such as Web or grid services. However, most existing strategies cannot handle well the problems of limited sample size and frequent turning points which are typical for the duration series of scientific workflow activities. To address such problems, we propose a novel pattern based time-series forecasting strategy which utilises a periodical sampling plan to build representative duration series, and then conducts time-series segmentation to discover the smallest pattern set and predicts the activity duration intervals with pattern matching results. The simulation experiment demonstrates the excellent performance of our segmentation algorithm and further shows the effectiveness of our strategy in the prediction of activity duration intervals, especially the ability of handling turning points.

The concept of cloud computing has been wide spreading very recently. Cloud computing has many unique advantages which can be utilised to facilitate (cloud) workflow execution. Transaction-intensive cost-constrained cloud workflows are workflows with a large number of workflow instances (i.e. transaction intensive) bounded by a certain budget for execution (i.e. cost constrained) in a cloud computing environment (i.e. cloud workflows). However, there are not any specific scheduling algorithms so far for transaction-intensive cost-constrained cloud workflows. This paper presents a novel scheduling algorithm which considers the characteristics of cloud computing to accommodate transaction-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly. The simulation performed demonstrates that the algorithm can reduce the mean execution cost while meeting the user-requested deadline.

Collaboration among enterprises through Web service has become a hot topic. Before the collaboration, how to select the most appropriate enterprise to collaborate with, from a set of enterprise candidates that provide similar functions, is an important issue. Existing work focus on proposing evaluation rules, and aggregating these rules to evaluate a service, where subjectiveness is usually involved. In this paper, we propose to utilize ¿serve, be served¿ relationship to evaluate the quality of services. In more detail, we use quality dependency graph (QDG) method model the relationship among enterprises, and then, by traveling the built QDG, an analytic hierarchy process (AHP) model is used to calculate the evaluation result of each candidate organization. Our method provides a more objective way for collaboration on enterprise level.

In grid workflow systems, temporal correctness is critical to assure the timely completion of grid workflow execution. To monitor and control the temporal correctness, fixed-time constraints are often assigned to a grid workflow and then verified. A checkpoint selection strategy is used to select checkpoints along grid workflow execution for verifying fixed-time constraints. The problem of existing representative strategies is that they do not differentiate fixed-time constraints as once a checkpoint is selected, they verify all fixed-time constraints. However, these checkpoints do not need to be taken for those constraints whose consistency can be deduced from others. The corresponding verification of such constraints is consequently unnecessary and can severely impact the efficiency of overall temporal verification. To address the problem, in this paper, we develop a new temporal dependency based checkpoint selection strategy which can select checkpoints according to different fixed-time constraints. With our strategy, the corresponding unnecessary verification can be avoided. The comparison and experimental simulation further demonstrate that our new strategy can improve the efficiency of overall temporal verification significantly over the existing representative strategies.

Editorial of the special issue of Concurrency and Computation: Practice and Experience, which contains selected high-quality papers from the 2nd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2007), which was held in Urumqi, China, on 17 August 2007.

Editorial of the special issue of Concurrency and Computation: Practice and Experience, which contains selected high-quality papers from the First International Workshop on Workflow Systems in Grid Environments (WSGE2006), held in the Hunan University of Science and Technology in China on 23 October 2006.

Abstract In a grid architecture, a grid workflow management system is a type of high‐level grid middleware which is supposed to support modelling, redesign and execution of large‐scale sophisticated scientific and business processes in many complex e‐science and e‐business applications. To ensure the correctness of grid workflow specification and execution, grid workflow verification and validation must be conducted. However, current research on grid workflow verification and validation is at the infancy stage and very few projects focus on them. Therefore, a systematic investigation and an overall classification of key issues in grid workflow verification and validation is helpful and should be presented so that we can keep on the right track and reduce unnecessary work as much as possible. As such, in this paper, we analyse the grid workflow verification and validation and present a taxonomy. Especially, we identify some important open points which are not discussed by the current research and hence need further investigation. The taxonomy is aimed at providing an overall picture of grid workflow verification and validation. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Scientific workflow execution often demands data‐centric and computation‐intensive collaboration efforts, which is typically different from the process‐centric workflow execution with fixed execution specifications. Scientific workflow execution often challenges the traditional workflow development strategy in dynamic context management and role definition. In view of this observation, application context spectrums are firstly distinguished from different profiles of scientific workflow development. Then, a role enactment strategy is proposed for enabling workflow execution in certain application context. They jointly enhance the validity of a scientific workflow development through clearly articulating the correlation between computational subjects and computational objects engaged in scientific workflow system. Furthermore, a novel context‐ and role‐driven scientific workflow development pattern is proposed for enacting a scientific workflow system on the Grid. Finally, a case study is presented to demonstrate the generic natures of the methods in this paper. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract With the rapid development of e‐business, workflow systems now have to deal with transaction‐intensive workflows whose main characteristic is the huge number of concurrent workflow instances. For such workflows, it is important to maximize the overall throughput to provide good quality of service. However, most of the existing scheduling algorithms are designed for scheduling of a single complex scientific workflow instance and are not efficient enough for scheduling transaction‐intensive workflows. To address this problem, we propose a throughput maximization strategy (TMS), which contains two specific algorithms for scheduling transaction‐intensive workflows at the instance and task levels, respectively. The first algorithm called Opposite Average Load tries to maximize the overall throughput by pursuing the overall load balance at the instance level, whereas the second algorithm called Extended Min–Min tries to further maximize the overall throughput at the task level by increasing the utilization rate of resources within each local autonomous group. The comparison and simulation performed on Swinburne Decentralized Workflow for Grid (SwinDeW‐G), a peer‐to‐peer‐based grid workflow environment, demonstrate that our strategy can improve the overall throughput significantly over existing scheduling algorithms when scheduling transaction‐intensive workflows. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract The time model for business cooperation across organizations and in business service grid has a special and important requirement, i.e. workday calendar model (WdCM). Workflow plays an important role in modelling business processes across multi‐enterprises or in service grid environments. However, current research in workflow time models does not pay sufficient attention to the differences in workday calendar between individual organizations. This paper aims to solve this problem by clarifying some basic concepts involved in the workday calendar models and presenting an XML‐based framework for workday calendar expressions, i.e. WdCM. Based on this WdCM, temporal constraints and the time optimization issue of workflow processes are investigated. Finally, a case study is analysed to demonstrate the feasibility of our workday calendar model. Copyright © 2007 John Wiley & Sons, Ltd.

Current service composition and coordination still remain at large amount of manual processing stage, which has brought about low efficiency. In this paper, we present an efficient algorithm for abstract service discovery and a service instance selection method. Our algorithm firstly builds up the special data structures of ontology concepts based on graph storage theories when publishing abstract services. Then, these data structures form a quick service query list. In our algorithm, the large number of ontology reasoning is processed at service publication stage, thus we can make sure the quick query response in service discovery without much reasoning. In addition, our service instance selection methods based on OWL QoS ontology can enable grid resource sharing and coordination more flexible.

Nowadays, grid and peer-to-peer (p2p) technologies have become popular solutions for large- scale resource sharing and system integration. For e- science workflow systems, grid is a convenient way of constructing new services by composing existing services, while p2p is an effective approach to eliminate the performance bottlenecks and enhance the scalability of the systems. However, existing workflow systems focus either on p2p or grid environments and therefore cannot take advantage of both technologies. It is desirable to incorporate the two technologies in workflow systems. SwinDeW-G (Swinburne Decentralised Workflow for Grid) is a novel hybrid decentralised workflow management system facilitating both grid and p2p technologies. It is derived from the former p2p based SwinDeW system but redeveloped as grid services with communications between peers conducted in a p2p fashion. This paper describes the system design and functions of the runtime environment of SwinDeW-G.

Abstract To verify fixed‐time constraints in Grid workflow systems, consistency and inconsistency conditions have been defined in conventional verification work. However, with a view of the run‐time uncertainty of activity completion duration, we argue that, although the conventional consistency condition is feasible, the conventional inconsistency condition is too restrictive and covers several different states. These states, which are handled conventionally by the same exception handling, should be handled differently for the purpose of cost saving. Therefore, in this paper, we divide conventional inconsistency into weak consistency, weak inconsistency and strong inconsistency and treat conventional consistency as strong consistency. Correspondingly, we develop some algorithms on how to verify them. Based on this, for weak consistency we present a method on how to adjust it to strong consistency by using mean activity time redundancy and temporal dependency between fixed‐time constraints. For weak inconsistency, we analyse briefly why it can be handled by simpler and more cost‐saving exception handling while for strong inconsistency, the conventional exception handling remains deployed. The final quantitative evaluation demonstrates that our research can achieve better cost‐effectiveness than the conventional work. Copyright © 2006 John Wiley & Sons, Ltd.

In grid workflow systems, a checkpoint selection strategy is responsible for selecting checkpoints for conducting temporal verification at the runtime execution stage. Existing representative checkpoint selection strategies often select some unnecessary checkpoints and omit some necessary ones because they cannot adapt to the dynamics and uncertainty of runtime activity completion duration. In this article, based on the dynamics and uncertainty of runtime activity completion duration, we develop a novel checkpoint selection strategy that can adaptively select not only necessary, but also sufficient checkpoints. Specifically, we introduce a new concept of minimum time redundancy as a key reference parameter for checkpoint selection. An important feature of minimum time redundancy is that it can adapt to the dynamics and uncertainty of runtime activity completion duration. We develop a method on how to achieve minimum time redundancy dynamically along grid workflow execution and investigate its relationships with temporal consistency. Based on the method and the relationships, we present our strategy and rigorously prove its necessity and sufficiency. The simulation evaluation further demonstrates experimentally such necessity and sufficiency and its significant improvement on checkpoint selection over other representative strategies.

In grid workflow systems, existing typical checkpoint selection strategies, which are used to select checkpoints for verifying fixed-time constraints at run-time execution stage, are not effective and/or efficient for fixed-time constraint verification because they often ignore some necessary checkpoints and select some unnecessary checkpoints. To improve such status, in this paper, we develop a new checkpoint selection strategy. Specifically, we first address a new concept of minimum proportional time redundancy which can be used to tolerate certain time deviation incurred by abnormal grid workflow execution. Then, we discuss relationships between minimum proportional time redundancy and fixed-time constraint consistency. Based on the relationships, we present our new strategy. With the strategy, we can avoid the omission of necessary checkpoints and the selection of excess unnecessary checkpoints. Consequently, our strategy is more effective and efficient for fixed-time constraint verification than the existing typical strategies. The final evaluation further demonstrates this result.

Conventional upper bound constraint verification in grid workflow systems is based on the key assumption that an upper bound constraint only has two states: consistency and inconsistency. However, due to complexity of grid workflows and dynamic availability of participating grid services, this assumption is too restrictive as there may be some intermediate states. Therefore, in this paper, we introduce four states for an upper bound constraint. Namely, we treat conventional consistency as strong consistency and divide conventional inconsistency into weak consistency, weak inconsistency and strong inconsistency. Correspondingly, we develop their verification methods. For weak consistency, we present some algorithms on how to adjust it to strong consistency without triggering exception handling as in conventional work. For weak inconsistency, we analyse why it can rely on less costly exception handling than conventional work. The final evaluation demonstrates that our four-state approach can achieve better cost-effectiveness than the conventional two-state approach.

Temporal verification is an important method to check the temporal correctness of workflow management systems (WfMSs). However, the current temporal verification is relatively independent of the workflow system environments. It does not pay sufficient attention to the interrelationship between the temporal verification at different stages, the mutual dependency between some temporal constraints, and the run-time checkpoint selection strategy, which hence hinders the incorporation and consistency between the temporal verification and the workflow system environments. In this paper, we effectively integrate the temporal verification at different stages and explore the dependency between some temporal constraints. In addition, we present a new effective runtime checkpoint selection strategy which dynamically selects appropriate checkpoints based on the activity completion duration. Furthermore, based on these analyses, we develop some new methods for more efficient temporal verification. These analyses and new methods help to eliminate the gap between the temporal verification and the workflow system environments.