Unmanned aerial vehicle (UAV) aided Mobile Edge Computing (MEC) has emerged as a promising technique to offer computing support for high-mobility and high-demand mobile devices (MDs). However, due to the dynamic scale of UAVs and MDs as well as their changeable resource availability and demands, it is very challenging to quickly make suitable UAV deployment plans for satisfying the real-time requirements. Existing solutions commonly adopt the centralized decision-making manner based on the global information, which leads to poor scalability, excessive search time, and repeated training costs. To address these important challenges, we propose a novel Federated deep Reinforcement learning based UAV Deployment optimization method (FRUD) for multi-UAV-aided MEC systems, aiming to minimize the average task response time via optimizing the real-time deployment locations of large-scale UAVs. In FRUD, each UAV independently conducts the deployment decision-making based on the local information of runtime environments rather than using the global information. Next, through feedback control and multi-UAV cooperation, an effective UAV deployment plan can be gradually formed. Simulation results show that the proposed FRUD well handles the UAV deployment problem in large-scale and dynamic multi-UAV-aided MEC systems and outperforms the state-of-art methods.

As organizations increasingly seek to build Foundation Models (FMs) using their own proprietary data, many are adopting private and in-house cloud infrastructures (often in addition to public clouds) to address concerns over cost, data privacy, and data sovereignty. However, these isolated private clouds frequently lack interoperability, creating barriers to cross-institutional collaboration, which is vital for training robust Domain-Specific Foundation Models (DSFMs) that rely on large and diverse datasets. Additionally, underutilized resources in private clouds lead to significant global energy inefficiencies. In this paper, we propose the Federated Large Domain Model System (FLDMS), a conceptual framework designed to facilitate collaborative foundation model development across multiple private cloud environments. We review the necessary enabling technologies, including decentralized protocols for data privacy and Large Language Models (LLMs) for automated orchestration, and present a high-level system design demonstrating how these components can be integrated. By enabling secure and efficient cross-organization cooperation, FLDMS provides a blueprint for building DSFMs while addressing the inefficiencies inherent in siloed private cloud systems.

The Cyber Threat Intelligence (CTI) marketplace is an emerging platform for CTI service requesters to countermeasure advanced cyber attacks, where CTI service providers are employed on payment. To create a trustworthy CTI marketplace environment, consortium-blockchain-based CTI service platforms have been widely proposed, where the blockchain system becomes the third role, crucially impacting the CTI service quality. How to sustainably promote CTI service quality in this tripartite marketplace is a challenging issue, which has not been well investigated in the literature. In this study, we propose a two-stage tripartite dynamic game-model-based incentive mechanism, where the participation incentives of the three parties are promoted under the constraints of Individual Rationality (IR) and Incentive Compatibility (IC). The sustainability of CTI service is quantitatively formalized through the CTI market demand, which impacts the future profits of the three parties. The Nash equilibrium of the proposed incentive mechanism is solved, where the CTI requester offers an optimal price to achieve effective defense against cyber attacks, and the blockchain platform and CTI service providers collaboratively contribute high-quality CTI services. Empirical experimental results show that the higher the quality of CTI services provided in the marketplace, the greater the market demand for CTI, resulting in a sustainable CTI marketplace.

Abstract In an era defined by the relentless influx of data from diverse sources, the ability to harness and extract valuable insights from streaming data has become paramount. The rapidly evolving realm of online learning techniques is tailored specifically for the unique challenges posed by streaming data. As the digital world continues to generate vast torrents of real-time data, understanding and effectively utilizing online learning approaches are pivotal for staying ahead in various domains. One of the primary goals of online learning is to continuously update the model with the most recent data trends while maintaining and improving the accuracy of previous trends. Based on the various types of feedback, online learning tasks can be divided into three categories: learning with full feedback, learning with limited feedback, and learning without feedback. This survey aims to identify and analyze the key challenges associated with online learning with full feedback, including concept drift, catastrophic forgetting, skewed learning, and network adaptation, while the other existing reviews mainly focus on a single challenge or two without considering other scenarios. This article also discusses the application and ethical implications of online learning. The results of this survey provide valuable insights for researchers and instructional designers seeking to create effective online learning experiences that incorporate full feedback while addressing the associated challenges. In the end, some conclusions, remarks, and future directions for the research community are provided based on the findings of this review.

In Mobile Edge Computing (MEC) systems, Unmanned Aerial Vehicles (UAVs) facilitate Edge Service Providers (ESPs) offering flexible resource provisioning with broader communication coverage and thus improving the Quality-of-Service (QoS). However, dynamic system states and various traffic patterns seriously hinder efficient cooperation among UAVs. Existing solutions commonly rely on prior system knowledge or complex neural network models, lacking adaptability and causing excessive overheads. To address these critical challenges, we propose the DisOff, a novel profit-aware cooperative offloading framework in UAV-enabled MEC with lightweight Deep Reinforcement Learning (DRL). First, we design an improved DRL with twin critic-networks and delay mechanism, which solves the Q-value overestimation and high variance and thus approximates the optimal UAV cooperative offloading and resource allocation. Next, we develop a new multi-teacher distillation mechanism for the proposed DRL model, where the policies of multiple UAVs are integrated into one DRL agent, compressing the model size while maintaining superior performance. Using the real-world datasets of user traffic, extensive experiments are conducted to validate the effectiveness of the proposed DisOff. Compared to benchmark methods, the DisOff enhances ESP profits while reducing the DRL model size and training costs.

The efficient and secure management of resources within flying ad-hoc networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the space-air–ground-integrated network (SAGIN), employing network virtualization (NV) technology in tandem with service function chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this article proposes a reinforcement learning algorithm that sets strict security-level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multilayer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics—namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio—are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3%, and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

Gradient inversion attacks can reconstruct the victim's private data once they have access to the victim's model and gradient. However, existing research is still immature, and many attacks are conducted in ideal conditions. It is unclear how damaging such attacks really are and how they can be effectively defended. In this paper, we first summarize the current relevant researches and their limitations. Then we design a general gradient inversion attack framework, which can attack both FedSGD and FedAVG. We propose approaches to enhance the label inference and image restoration, respectively. Our approach surpasses the SOTA attacks, by successfully attacking the batches from ImageNet while other methods fail to attack. Finally, we suggest several defense strategies without any utility loss from extensive experiments. We are confirmed that our work makes people aware of the privacy issues and can actively avoid the potential risks.

The space-air-ground-ocean integrated network (SAGOI-Net) has become the focus of research in recent years, which has the characteristics of wide coverage and strong adaptability. However, due to the influence of multiple heterogeneous network segments, this network is unable to provide excellent quality of service (QoS). Based on the software-defined network and virtual network architecture, we abstract SAGOI-Net as a three-layer heterogeneous physical network resource, and propose a multi-domain virtual network embedding solution to optimize QoS. Specifically, before virtual network embedding, we collected SAGOI-Net's resource information through software-defined network and modeled it. In the virtual network embedding process, we first classify the virtual network request through K-means, and dynamically adjust the reward function to use reinforcement learning to solve the optimal virtual network embedding strategy. Finally, simulation experiments verify the effectiveness of the scheme.

Federated Learning is a machine learning methodology that emphasizes data privacy, involving minimal interaction with each other’s systems, primarily exchanging model parameters. However, this approach can introduce challenges in system development and operation because it inherently faces statistical and system heterogeneity issues. The diverse data storage formats and system environments across clients limit the feasibility of training with a uniform code. To distribute a new code to each environment, active participation of Federated Learning collaborators is necessary, incurring time and cost. Moreover, it impedes adopting modern automated development and deployment paradigms such as DevOps or MLOps. This study investigates how Large Language Models (LLMs) can automatically tailor a single code to individual client environments in heterogeneous scenarios without human intervention. Moreover, to enable the automatic adaptation of the deployed code for conducting new experiments within the system, it is imperative to assess the presence of potentially malicious code that could jeopardize data security. To address this challenge, we introduce a novel prompt engineering technique to enhance LLMs’ detection capabilities, thereby bolstering our ability to detect malicious code effectively.

Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high. By synthesizing datasets with high information density, dataset distillation offers a range of potential applications, including support for continual learning, neural architecture search, and privacy protection. Despite recent advances, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of dataset distillation, characterizing existing approaches, and then systematically reviewing the data modalities, and related applications. In addition, we summarize the challenges and discuss future directions for this field of research.

Mobile edge computing (MEC) relieves the latency and energy consumption of mobile applications by offloading computation-intensive tasks to nearby edges. In wireless metropolitan area networks (WMANs), edges can better provide computing services via advanced communication technologies. For improving the Quality-of-Service (QoS), edges need to be collaborated rather than working alone. However, the existing solutions of multiedge collaboration solely adopt a centralized or decentralized decision-making way of load balancing, making it hard to achieve the optimal result because the local and global conditions are not jointly considered. To solve this problem, we propose a novel two-stage decision-making method of load balancing for multiedge collaboration (TDB-EC). First, the centralized decision making is executed with global information, where a deep neural networks (DNNs)-based prediction model is designed to evaluate the range of task scheduling between adjacent edges. Next, considering the global condition of load balancing, the decentralized decision making is executed with local information, where a deep$Q$-networks (DQN)-based$Q$-value prediction model of adjustment operations is developed to evaluate the load balancing plan among edges. Finally, the objective load balancing plan is obtained via feedback control. Extensive simulation experiments demonstrate the adaptability of the TDB-EC to various scenarios of multiedge load balancing, which approximates the optimal result and outperforms three classic methods.

As the workloads and service requests in cloud computing environments change constantly, cloud-based software services need to adaptively allocate resources for ensuring the Quality-of-Service (QoS) while reducing resource costs. However, it is very challenging to achieve adaptive resource allocation for cloud-based software services with complex and variable system states. Most of the existing methods only consider the current condition of workloads, and thus cannot well adapt to real-world cloud environments subject to fluctuating workloads. To address this challenge, we propose a novel Deep Reinforcement learning based resource Allocation method with workload-time Windows (DRAW) for cloud-based software services that considers both the current and future workloads in the resource allocation process. Specifically, an original Deep Q-Network (DQN) based prediction model of management operations is trained based on workload-time windows, which can be used to predict appropriate management operations under different system states. Next, a new feedback-control mechanism is designed to construct the objective resource allocation plan under the current system state through iterative execution of management operations. Extensive simulation results demonstrate that the prediction accuracy of management operations generated by the proposed DRAW method can reach 90.69%. Moreover, the DRAW can achieve the optimal/near-optimal performance and outperform other classic methods by 3$\sim$13% under different scenarios.

Microwave Power Transfer (MPT) is a promising technology to charge sensor devices (SDs) wirelessly in wireless sensor networks, and Cloud Computing (CC) can significantly promote task processing capacity of SDs. However, the propagation loss can dramatically influence the harvested energy and computation performance. So, for wireless sensor networks, we study an unmanned aerial vehicle-assisted cloud wireless charging system with the cooperation of the cloud server and the unmanned aerial vehicle (UAV). First, the UAV acts as the energy transmitter, and we design a quantitative charging scheme according to the energy-aware of SDs’ battery capacity. Second, the cloud server processes the tasks uploaded by SDs with the cooperation of the UAV, and we consider the communication connection between the cloud server and the UAV. Third, we propose the joint resource-trajectory optimization to reduce the energy consumption of UAVs. We put forward the Chaotically Adaptive Beetle Swarm Optimization Based on Cauchy Mutation (CABSOC) assisted block coordinate descent algorithm for addressing this non-convex problem. Numerical results indicate that the proposed solution can significantly improve the energy performance of the UAV. And the energy consumption is reduced by 11% compared with the solution with network function virtualization (NFV).

In this article, we proposedualtraceabledistributedattributebasedencryption withsubsetkeywordsearch system (DT-DABE-SKS, abbreviated as$\mathcal {DT}$) to simultaneously realize data source trace (secure provenance) and user trace (traitor trace) and flexible subset keyword search from polynomial interpolation. Leveraging non-interactive zero-knowledge proof technology,$\mathcal {DT}$preserves privacy for both data providers and users in normal circumstances, but a trusted authority can disclose their real identities if necessary, such as the providers deceitfully uploading false data or users maliciously leaking secret attribute key. Next, we introduce the new conception of updatable and transferable message-lock encryption (UT-MLE) for block-level dynamic encrypted file update, where the owner does not have to download the whole ciphertext, decrypt, re-encrypt and upload for minor document modifications. In addition, the owner is permitted to transfer file ownership to other system customers with efficient computation in an authenticated manner. A nontrivial integration of$\mathcal {DT}$and UT-MLE lead to the distributed ABSE with ownership transfer system ($\mathcal {DTOT}$) to enjoy the above merits. We formally define$\mathcal {DT}$, UT-MLE, and their security model. Then, the instantiations of$\mathcal {DT}$and UT-MLE, and the formal security proof are presented. Comprehensive comparison and experimental analysis based on real dataset affirm their feasibility.

The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, e.g., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradients, parameters, or activations, etc. However, there is little research on collaborative learning based on state-of-the-art and large-scaled models, mainly due to the high computation or communication overheads of the latest models. Training these models may be still unrealized for users’ terminals. In this paper, we make a first attempt at the sensitive image pre-training with large-scaled models in the collaborative learning scenario, and propose a new lightweight framework for split learning based on mask, Masked Split Learning (MaskSL). We further ensure its security by differential privacy. Besides, we model the computation and communication overheads of several collaborative learning approaches by deduction to illustrate advantages of our scheme. Finally, we design and conduct a series of experiments on real-world datasets, e.g., in face recognition and medical image classification tasks, to demonstrate the performance of MaskSL.

Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This “all-or-nothing” authorization mode is not compatible with the “pay-as-you-use” commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.

Today, the global economy is dependent on the Internet and computational resources. Although they are tightly interconnected, it is difficult to evaluate their degree of interdependence. Keeping up with the pace of technology can be a challenging task, mainly when updating the hardware and software infrastructure. Every day, corporations and governments are faced with this issue; most have been victims of cyber attacks, security breaches, and data leaks. The consequences are significant in monetary losses; damage remediation is unattainable, even impossible, in certain circumstances. The repercussions might include reputational damage, legal responsibility, and threats to national security (when attacks are carried out against critical infrastructures to control the resources of a country), to name a few. Similarly, data has become such an integral part of many industries that it is one of the most critical targets for attackers that often is encrypted by ransomware, stolen, or corrupted. Without data, many companies are not able to continue operating as they do. The combination of all these factors complicates the ability of organizations to cooperate, trust, and share information in efforts to research and develop solutions for industry and government.This work proposes a Blockchain-based infrastructure solution provided by “Hyperledger Fabric” technology for companies to securely transmit and share information using the latest encryption and data storage technologies operating on the model of distributed systems and smart contracts. By presenting unique digital assets as Non-Fungible Tokens (NFT), the infrastructure is able to trust the integrity of the data, while protecting it from counterfeiting. Through the use of a Blockchain-based file storage system known as IPFS, and by connecting all the relevant elements together through a web-based application, it is possible to demonstrate that the implementation of such systems is feasible, highly scalable and a useful tool that many organizations can utilize to create new work systems and worktflows for digital asset management.

Data-centric workflows across organizations are gaining more and more popularity. To automate this process, the traditional approaches centralise related data from different organizations to the cloud, and then use a workflow engine to complete cross-organizational collaboration. There are limitations of those approaches, such as the requirement of data centralisation which could lead to the leakage of critical data. In this work, we present a workflow platform for consuming distributed data based on Kubernetes and JupyterFlow, and use blockchain technology to guarantee security and privacy through empowering the data owner with control of their own data. To reduce replication and network throughput, the blockchain only contains the meta data referring to the data in off-chain storage. We develope a JupyterHub extension to support data registration and query, and used the RESTFul API to connect the web application with the blockchain network. Finally, we demonstrate a simple data processing use case as proof-of-concept to validate our proposed platform.

The diversity (text, images, algorithms, etc.) and the ambiguity of data sovereignty and privacy make the management of digital objects very challenging. Users need a unified and convenient way to manage their digital objects. This places a high demand on the findability and interoperability of the management model. In recent years blockchain has provided a new route to an open and secure platform due to its attributes such as distributed, traceable, and tamper-evident. In this paper, a new NFT-like scheme is proposed, which uses metadata converts digital assets into digital object identifiers, and transforms digital objects that require clear sovereignty into NFTs to ensure the authenticity and uniqueness of ownership. Our scheme can facilitate the dynamic management of digital objects using smart contracts.

Lack of trustworthiness, access policy flexibility, and user privacy preservation in centralized access control systems raise numerous security issues and reduce the collaboration maturity of global data sharing systems. In this paper, we propose a Self-Sovereign Identity-based, Decentralized, and Dynamic (SSIDD) access control system. SSIDD utilizes blockchain technologies to build trust for untrusted data sharing networks and ensures user privacy. Our access control provides high access policy flexibility and security for global inter-enterprise collaborations from a diverse industrial environment. SSIDD authenticates its users based on their Decentralized Identifiers (DID), which are under control of users and can be resolved into a DID document stored on the blockchain. Our data management technology keeps the data sharing systems safe against issues such as data breaches, identity thefts, and privacy violations. Besides, the authorization process of SSIDD is dynamic by adopting several smart contracts. The transparency of rules and agreements in smart contracts and the traceability of records on blockchain ledger provide a high level of security and trust. For proof of concept, we have developed and evaluated a prototype of SSIDD. Our evaluations show that the throughput and latency of our method are within an acceptable range.

The demand for electricity is increasing exponentially day by day, especially with the arrival of electric vehicles. In the smart community neighborhood project, electricity should be produced at the household or community level and sold or bought according to the demands. Since the actors can produce, sell, and buy according to the demands, thus the name prosumers. ICT solutions can contribute to this in several ways, such as machine learning for analyzing the household data for customer demand and peak hours for the usage of electricity, blockchain as a trustworthy platform for selling or buying, data hub, and ensuring data security and privacy of prosumers. TOTEM: Token for controlled computation is a framework that allows users to analyze the data without moving the data from the data owner's environment. It also ensures the data security and privacy of the data. Here, in this article, we will show the importance of the TOTEM architecture in the EnergiX project and how the extended version of TOTEM can be efficiently merged with the demands of the current and similar projects.

Abstract A redactable consortium blockchain (RCB) can build a trust layer for industrial internet of things (IIoT) so as to enable IIoT to resist certain powerful attacks resulting in improper block content. The redactability is particularly important for blockchains applied in IIoT with valuable or sensitive activities such as financial IoT or energy-trading IoT. Huang et al. proposed a threshold chameleon hash (TCH) scheme and then constructed an accountable-and-sanitizable chameleon signature scheme based on TCH. These two primitives are further used as fundamental modules to build an RCB, which empowers IIoT devices to operate the blockchain in a controllable way. However, our paper shows that Huang et al.’s RCB suffers from a security problem that weakens the crucial redactability. Specifically, we find out that if a transaction in a given block is legally redacted by all authorized sensors who collectively hold the private redacting key, anyone (without any private information) can further redact this redacted transaction and delete any transaction within this redacted block and, meanwhile, any sensor user with a private signing (not redacting) key can insert a forged transaction into this redacted block. We further address this threat by replacing the TCH module in Huang et al.’s RCB with our designed TCH.

Abstract The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

With time-varying workloads and service requests, cloud-based software services necessitate adaptive resource allocation for guaranteeing Quality-of-Service (QoS) and reducing resource costs. However, due to the ever-changing system states, resource allocation for cloud-based software services faces huge challenges in dynamics and complexity. The traditional approaches mostly rely on expert knowledge or numerous iterations, which might lead to weak adaptiveness and extra costs. Moreover, existing RL-based methods target the environment with the fixed workload, and thus they are unable to effectively fit in the real-world scenarios with variable workloads. To address these important challenges, we propose a Prediction-enabled feedback Control with Reinforcement learning based resource Allocation (PCRA) method. First, a novel Q-value prediction model is designed to predict the values of management operations (by Q-values) at different system states. The model uses multiple prediction learners for making accurate Q-value prediction by integrating the Q-learning algorithm. Next, the objective resource allocation plans can be found by using a new feedback-control based decision-making algorithm. Using the RUBiS benchmark, simulation results demonstrate that the PCRA chooses the management operations of resource allocation with 93.7 percent correctness. Moreover, the PCRA achieves optimal/near-optimal performance, and it outperforms the classic ML-based and rule-based methods by 5$\sim$∼7% and 10$\sim$∼13%, respectively.

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the semantics among texts to achieve semantic-aware multi-keyword search. However, the LDA topic model treats queries and documents from the perspective of topics, and the keywords information is ignored. In this article, we propose a privacy-preserving searchable encryption scheme based on the LDA topic model and the query likelihood model. We extract the feature keywords from the document using the LDA-based Information Gain (IG) and Topic Frequency-Inverse Topic Frequency (TF-ITF) model. With feature keyword extraction and the query likelihood model, our scheme can achieve a more accurate semantic-aware keyword search. A special index tree is used to enhance search efficiency. The secure inner product operation is utilized to implement the privacy-preserving ranked search. The experiments on real-world datasets demonstrate the effectiveness of our scheme.

In this article, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to cloud to deliver telemedicine service. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of patient’s illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analysis to evaluate the efficiency.

Byzantine Fault Tolerant (BFT) state machine replication protocols are used to achieve agreement among replicated servers with arbitrary faults. Most existing BFT protocols perform well in fault-free cases, but usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes graceful performance degradation in faulty cases. The major novelty of DBFT lies in the double-response mechanism, which lets replica nodes deterministically respond to clients twice: one is after the speculative execution phase and the other is after the commitment phase. The double-response mechanism ensures good performance in spite of inconsistency in speculative execution. Also, to further alleviates undetectable performance attacks by a smartly malicious primary, we change primary upon every outstanding request. Moreover, DBFT does not involve clients in critical consensus operations so as to reduce the load of clients. We prove the correctness properties, i.e., safety and liveness, of DBFT. We conduct extensive experiments and the results show that, DBFT outperforms similar BFT protocols obviously in normal cases.

In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuitable for federated learning and vulnerable to security risks. To mitigate these issues, we propose a privacy-preserving oriented no trusted third party federated learning system based on blockchain (NttpFL). The initiator of the federated learning task and the partners negotiate keys through the conference key agreement and do not need to distribute keys through a trusted third party. We design a double-layer encryption mechanism to ensure privacy. Partners cannot obtain any private information other than their information. The decentralized nature of blockchain suits our system. In addition, blockchain makes the entire process transparent and traceable and avoids the single node failure problem. Experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.

We have entered the era of big data, and it is considered to be the ”fuel” for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals’ privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.

Quality Critical Decentralised Applications (QC-DApp) have high requirements for system performance and service quality, involve heterogeneous infrastructures (Clouds, Fogs, Edges and IoT), and rely on the trustworthy collaborations among participants of data sources and infrastructure providers to deliver their business value. The development of the QCDApp has to tackle the low-performance challenge of the current blockchain technologies due to the low collaboration efficiency among distributed peers for consensus. On the other hand, the resilience of the Cloud has enabled significant advances in software-defined storage, networking, infrastructure, and every technology; however, those rich programmabilities of infrastructure (in particular, the advances of new hardware accelerators in the infrastructures) can still not be effectively utilised for QCDApp due to lack of suitable architecture and programming model.

Trading and storage of renewable energy offers a way for the prosumer to extract value from the surplus energy that they produce, while also mitigating energy shortfall. Power companies can enlist prosumers in demand response strategies for grid stability and cost savings. We present RenewLedger, a blockchain-based framework for renewable energy transaction, storage management and direct-to-consumer demand response incentivization and gamification for peak shaving. We design and implement this system using Hyperledger Fabric and report on performance benchmarking experiments conducted using Hyperledger Caliper.

The popularization and rapid development of distributed energy becomes a trend of the times. Distributed energy prosumers should be able to trade with each other to reduce losses, increase efficiency, flexibility and economy. The traditional centralized power settlement scheme is not suitable for the utilization on the situation of distributed energy transaction settlement. Energy internet as the next generation energy system integrating cuttingedge information technologies with energy system could realize peerto- peer energy services. The distributed interactive concept of the energy trading is highly consistent with the principle of blockchain. In this paper, aiming at the problems of information disunity, trust system difficult to establish, power deviation waste and cost advance caused by power pre-sale, a power transaction asynchronous settlement system for microgrid is proposed based on blockchain technology. The experiment results illustrate that the system obtains promising performance by reasonable set grid structure which could meet the requirements of practical applications.

Internet of Things (IoT) has become a critical infrastructure in smart city services. Unlike traditional network nodes, most of the current IoT devices are constrained with limited capabilities. Moreover, frequent changes in the network status (e.g., nodes turns into the sleep mode to save battery) make it even more difficult to set up a stable, secure transmission among smart city IoT devices. On the one hand, these weaknesses make the IoT more vulnerable to attacks, such as data eavesdropping, which can monitor, tamper, and obtain the transporting data. On the other hand, the high-confidence smart city service strongly relies on the security of data transporting among the IoT devices, e.g., data being tempered would reduce the reliability of smart city services and data being monitored or stolen would infringe the privacy of smart city services. Toward high-confidence smart city IoT services, we proposed an approach to secure the data transportation among the smart city IoT devices, which combines a k-n secret-sharing mechanism and software-defined networking (SDN) technique to securely transport IoT data. Specifically, the data are transported by multiple routes calculated by the SDN controller adaptively. Data safety is guaranteed by the all-or-nothing feature of the k-n secret-sharing mechanism. Two SDN-based transmission strategies, which leverage the SDN's advantages on network management, and scheduling, are applied to overcome the challenges of the unstable network state in IoT. Extensive experiments conducted from many aspects show that the proposed approach can remarkably reduce the attack success rate with reasonable and acceptable overhead.

Secure search over encrypted remote data is crucial in cloud computing to guarantee the data privacy and usability. To prevent unauthorized data usage, fine-grained access control is necessary in multi-user system. However, authorized user may intentionally leak the secret key for financial benefit. Thus, tracing and revoking the malicious user who abuses secret key needs to be solved imminently. In this paper, we propose an escrow free traceable attribute based multiple keywords subset search system with verifiable outsourced decryption (EF-TAMKS-VOD). The key escrow free mechanism could effectively prevent the key generation centre (KGC) from unscrupulously searching and decrypting all encrypted files of users. Also, the decryption process only requires ultra lightweight computation, which is a desirable feature for energy-limited devices. In addition, efficient user revocation is enabled after the malicious user is figured out. Moreover, the proposed system is able to support flexible number of attributes rather than polynomial bounded. Flexible multiple keyword subset search pattern is realized, and the change of the query keywords order does not affect the search result. Security analysis indicates that EF-TAMKS-VOD is provably secure. Efficiency analysis and experimental results show that EF-TAMKS-VOD improves the efficiency and greatly reduces the computation overhead of users' terminals.

Cloud computing provides flexible data management and ubiquitous data access. However, the storage service provided by cloud server is not fully trusted by customers. Searchable encryption could simultaneously provide the functions of confidentiality protection and privacy-preserving data retrieval, which is a vital tool for secure storage. In this paper, we propose an efficient large universe regular language searchable encryption scheme for the cloud, which is privacy-preserving and secure against the off-line keyword guessing attack (KGA). A notable highlight of the proposal over other existing schemes is that it supports the regular language encryption and deterministic finite automata (DFA) based data retrieval. The large universe construction ensures the extendability of the system, in which the symbol set does not need to be predefined. Multiple users are supported in the system, and the user could generate a DFA token using his own private key without interacting with the key generation center. Furthermore, the concrete scheme is efficient and formally proved secure in standard model. Extensive comparison and simulation show that this scheme has function and performance superior than other schemes.

Blockchain, as an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, has attracted intensive attention in both research and applications recently. Blockchain, especially powered by chain-coded smart contracts, has the full potential of revolutionizing increasingly centralized cyber-physical-social systems (CPSSs) for constructions and applications, and reshaping traditional knowledge automation workflows. The key advantage of blockchain technology lies in the fact that it can enable the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources.

The surging interest in blockchain has revitalized the search for efficient Byzantine fault-tolerant (BFT) protocols, which are used for blockchains to achieve consensus among replicated data blocks. Most existing BFT protocols perform well in fault-free cases, but they usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes graceful performance degradation in normal cases. The major novelty of DBFT lies in the double-response mechanism, which lets replica nodes deterministically respond to clients twice: one is after the speculative execution phase and the other is after the commitment phase. The double-response mechanism can handle inconsistency in speculative execution, so as to alleviate performance degradation caused by faults. Moreover, DBFT does not involve clients in critical consensus operations so as to reduce the load of clients. The correctness properties, i.e., safety and liveness, of DBFT is rigorously proved. The performance of DBFT is evaluated via experiments and the results show that, DBFT outperforms similar BFT protocols obviously in normal cases.

The purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the Ninth “Green Cloud Computing: Methodology and Practice,” held in conjunction with SC'14. This annual conference brings together practitioners, researchers, students, and scholars interested in updating their knowledge about or active in green cloud computing, in order to foster state-of-the-art research in the area of green cloud computing including the topics of modeling, algorithm development, implementation, and execution. Although cloud computing has been widely adopted by the industry, it still suffers from different challenging issues, one of which is efficient energy management. The cloud may comprise thousands of servers, network devices, and disks and typically serve millions of users globally. Such a large-scale data center will consume considerable amount of energy. Therefore, optimizing the efficiency of the application's use of cloud resources and improving the energy efficiency in cloud, without sacrificing Service Level Agreements (SLA), cannot only save significant budget for cloud users and owners but also make a significant contribution to greater environmental sustainability. This special issue will serve as a landmark source for education, information, and reference to practitioners, researchers, students, and scholars. This issue would be incomplete without in-VM management and it is very useful in green cloud computing because it provides the abilities of in-VM monitoring, VM reconfiguration, and performance measurement. Zhan et al3 propose a secure automated in-VM management approach, a hypervisor-based shell managing the VMs in an out-of-box way. In addition, it presents a dummy process selection and a system call injection method to further enhance the system security and transparency. Large-scale data processing is a problem that must be faced in green cloud computing. Ding et al4 consider the data structure of the graph and propose an index structure named Closure+-tree to process the subgraph query efficiently. Wang et al5 consider the shortcoming of collaborative filtering in processing large-scale data and propose a new method for training autoencoder-based CF. We encourage the reader to review the works of Ye et al,6 Chen et al,7 and He et al8 to get more information about the topics of modeling, algorithm development, implementation, and execution.

As an innovated and revolutionized technology, blockchain has been applied in many fields, such as cryptocurrency, food traceability, identity management, or even market prediction. To discover its great potential, both industry and academia have paid great attention to it and numerous researches have been conducted. Based on the literature and industry whitepapers, in this survey, we unroll and structure the blockchain related discoveries and scientific results in many aspects. Particularly, we classify blockchain technologies into four layers and carry out a comprehensive study on the consensus strategies, the network, and the applications of blockchain. Different blockchain applications are put into the corresponding categories based on the fields, especially in Internet of Things (IoT). When introducing each layer, we not only organize and summarize the related works, but also discuss the fundamental issues and future research directions. We hope this survey could shed some light on the research of blockchain and serve as a guide for further studies.

Internet of Things (IoT), coming with billions of connected devices, could potentially transform our daily life but could also create a serious security headache. It brings greater complications in securely accessing these devices with privacy protection guaranteed, and several research issues need to be investigated in detail, e.g., access control, traceability, anonymity, authentication, security bootstrap, etc. Most of the traditional security protection mechanisms are centralized, which make them difficult to scale up to meet the security demands of the IoT.

High performance computing systems comprised of hundreds or thousands of computational nodes can generate a high volume of system log entries at a high data velocity. Analyzing these logs soon after they are generated is a significant challenge, due to the complexity of log messages, the speed at which they are produced, and the lack of a method to quickly map or categorize messages to meaningful sets. The impact of this problem is that it is not possible to comprehensively glean timely information from logs about the overall system or the health of individual nodes. In this paper, we address this problem through the development of a novel approach for system log analysis based on a markov random field (MRF) that can quickly categorize system log messages into multiple categories based on representative training examples provided by a user. We present a theoretical model of our approach, followed by an extensive evaluation of the accuracy and performance of the implementation of our model. We found that our MRF based approach can quickly categorize system log messages with a high degree of accuracy.

Time flies fast, it has been already one year since I was appointed as the Editor-in-Chief of this great publication, and thanks to the strong support and dedication of our associate editors, editorial staff, anonymous reviewers, and authors, we have made solid progress and I really enjoy my work and our achievement so far. At this point, significant improvements in the timeliness and quality of the review process, as well as the numbers of manuscripts submitted and articles published have been accomplished.

Summary Cloud computing has become increasingly popular, which has led many individuals and organizations towards cloud storage systems. This move is motivated by benefits such as shared storage, computation, and transparent service among a massive number of users. However, cloud‐computing systems require the maintenance of complex and large‐scale systems with practically unavoidable runtime problems caused by hardware and software faults. Large systems are very complex due to heterogeneity, dynamicity, scalability, hidden complexity, and time limitations. Automatic anomaly detection is a critical technique for managing such complex cloud resources. This paper proposes a scalable model for automatic anomaly detection on a large system like a cloud. The anomaly detection process is capable of issuing a correct early warning of unusual behavior in dynamic environments after learning the system characteristic of normal operation. To detect unusual activity in the cloud, we need to monitor the data center and collect cloud performance logs. In this paper, we propose an adaptive anomaly detection mechanism, which investigates principal components of the performance metrics. It transforms the performance metrics into a low‐rank matrix and calculates the orthogonal distance using the Robust PCA algorithm. The proposed model updates itself recursively, while learning and adjusting the new threshold value, to minimize reconstruction errors. This paper also investigates robust principal component analysis in distributed environments using Apache Spark as the underlying framework. It specifically addresses cases in which normal operation might exhibit multiple hidden modes. The accuracy and sensitivity of the model were tested on Amazon CloudWatch datasets, and Yahoo! datasets. The model achieved an accuracy of 88.54%.

A Gaussian integer is a complex number whose real and imaginary parts are both integers. This paper proposed a unified construction of perfect Gaussian integer sequences based on cyclic difference sets. It turns out that this construction produces an abundance of perfect Gaussian integer sequences. The proposed construction includes all the sequences recently given by Lee et. al as special cases, and many new families of Gaussian integer sequences. To illustrate, two classes of perfect Gaussian integer sequences defined from Kasami-Welch functions and Helleseth-Gong functions are given.

This paper proposes a novel and scalable model for automatic anomaly detection on a large system such as a cloud. Anomaly detection issues early warning of unusual behavior in dynamic environments by learning system characteristic from normal operational data. Anomaly detection in large systems is difficult to detect due heterogeneity, dynamicity, scalability, hidden complexity, and time limitation. To detect anomalous activity in the cloud, we need to monitor the datacenter and collect cloud performance data. In this paper, we propose an adaptive anomaly detection mechanism which investigates principal components of performance metrics. It transforms the performance metrics into a low-rank matrix and then calculates the orthogonal distance using the Robust PCA algorithm. The proposed model updates itself recursively learning and adjusting the new threshold value in order to minimize reconstruction errors. This paper also investigates the robust principal component analysis in distributed environments using Apache Spark as the underlying framework, specifically addressing cases in which a normal operation might exhibit multiple hidden modes. The accuracy and sensitivity of the model is tested on Google data center traces and Yahoo! datasets. The model achieves an 87.24% accuracy.

The internet of Things (IoT) and Wireless Sensor Networks (WSNs) benefit smart home implementation. This paper introduces an inner sensor alarm system, which can send alarm messages as well as evidence material to outer media. Our system provides a checking function for real time exceptional events, while sending the alarm massage to the user. This application has three gradations: Client Sensor, Data Collector and Data Center. Client Sensor creates data, Data Collector collects and analyzes data and Data Center stores the alarm data materials. We build the network topology, communication model, data synchronous model and data storage mode to ensure the efficiency of the operation. Data transportation is considered carefully, so only the alarm data can be sent to the Data Center. Alarm messages are checked by user finally. This man checking procedure reduces the alarm false positive rate, and saves the common resource observably.

With the completion of the Human Genome Project, proteomics research has become one of the most important topics in the fields of life science and natural science. The project determined that proteins participate in life activities mainly in the form of complexes. At present, research on protein-protein interaction networks (PPINs) have mainly focused on detecting protein complexes or function modules. This problem has been transformed into a recognizable dense subgraph problem in a PPIN diagram.The situation in PPIN research in recent years is introduced in this study, including commonly used databases, traditional detection algorithms, recent solutions, and the application of the swarm intelligence algorithms in this field. We then propose a detection scheme based on particle swarm optimization (PSO) and gene ontology knowledge. This scheme combines PSO and biological gene ontology knowledge to identify complexes from PPINs. Simultaneously, network topology knowledge improves the detection accuracy of the protein module.

The field of Big Data is particularly challenging in practical aspects of data collection, data analysis and data storage and has massive consequential implications for the future. The scope and scale of Big Data frequently necessitate the processing power attributed to High Performance Computing (HPC) and the scalability of Cloud Computing for processing and storage. Source topics for Big Data are continually expanding and include diverse topic areas of finance, medical, particle physics and social interaction and everything in between. Effective Big Data analysis encompasses the need to minimise data sets by identifying, analysing and storing only significant data; recognising and separating the relevant and irrelevant data provide many research opportunities in itself. The outcomes of Big Data analysis may explain past events and trends, suggest controls that are necessary for the present or predict a future world in terms of preparation, planning or positioning. This special issue will further publicise and promote this immensely import field of research with goals of creating a public record of achievements so far and providing inspiration for even greater developments in the coming years. Computational costs associated with Cloud Computing can be significant. In the paper ‘Online optimization scheduling for scientific workflows with deadline constraint on hybrid clouds’ 1, Bing Lin, Wenzhong Guo and Xiuyan Lin examine scheduling strategies and propose ‘hierarchical iterative application partition’ (HIAP) as an algorithm to increase the number of workflows completed within a given timeframe, thereby reducing overall costs. Ensuring the integrity of transmitted data is of paramount importance for data analysis, the paper ‘A MapReduce based Parallel K-Means Clustering for Large Scale CIM Data Verification’ 2 discusses the topic and presents a parallel K-means clustering algorithm for large scale Common Information Model (CIM) data verification. The paper concludes that time saving is achievable using parallel K-means while generating a high level of precision in data verification. ‘Bursty Event Detection from Microblog: A Distributed and Incremental Approach’ 3 is a practical example of the use of Big Data analysis. The researchers propose a method of bursty event detection, BEE+, as a means of tracking ‘topic drift’ from a microblog dataset of over 6 million posts. The amount of data collected and necessary storage rate are frequent considered to be problems for Big Data systems. ‘Performance Evaluation of a Distributed Storage Service in Community Network Clouds’ 4 compares the write and read capability of Tahoe-LAFS storage system is when deployed on community clouds and commercial systems. The paper concludes that write speeds are comparable, while read speeds were better in the commercial system. The information content of Big Data can have a significant commercial value. The cost to analyse data can be very high while the act of data collection can be both expensive and time consuming; loss of data to a competitor or invalidation because of falsification could result in commercial collapse of a business. ‘Secure Cryptographic Functions via Virtualization-based Outsourced Computing’ 5 considers the use of cryptography to protect data and, more fundamentally, suggests a method for the protection of the cryptographic system and process. In ‘Towards an Autonomous Decentralised Orchestration System’ 6, the authors propose distributed execution engines which exploit the benefits of parallel computation in the workflow to improve overall execution time. The paper provides an evaluation of the system and demonstrates the scalability benefit of the decentralised system. The limitations of Cloud related simulation tools are the subject of ‘Multi-layered simulations at the heart of workflow enactment on Clouds’ 7. The authors suggest that a multistage approach is advantageous in resolving some of the issues of scalability and scope without adversely affecting the performance of the workflow execution simulation. The continuing expansion of the Internet has presented ever greater challenges to crawler services used to collect information for indexing. ‘A Task Scheduling Strategy based on Weighted Round-Robin for Distributed Crawler’ 8 presents an implementation of a multithread distributed crawler which is scalable and fault tolerant. The paper includes experiments which indicate that the system exhibits good load balancing performance. The ability to adapt to changes of tenant requirements and cloud services is investigated in ‘Cross-Clouds Services Autonomic Management Approach based on Self-Organising Multi-Agent Technology’ 9. The research proposes a method where cloud services are managed by a series of autonomous agents which interact with each other to obtain macro-level service aggregation. The efficiency and usability of the proposed approach are confirmed with experimental results using public data sets. ‘Bilinear-map Accumulator based Verifiable Intersection Operations on Encrypted Data in Cloud’ 10 investigates the problem of conducting set-intersection operations on the ciphertext sets in the Cloud without the capability of decryption. The research opposed a model, called VIOEDC, to address this problem. The correctness and the security properties of the model have been approved in this paper. [Correction added on 07 June 2016, after first online publication: this paragraph has been added.] The papers presented in this special issue show that the subject arena of Big Data and Smart Computing continues to offer many diverse opportunities for research and investigation. It is anticipated that this special issue papers will provide a foundation for further work in the future by the authors and by many other researchers.

Summary Correlation analysis is both popular and useful in a number of social networking research, particularly in the exploratory data analysis. In this paper, three well‐known and often‐used correlation coefficients, Pearson product–moment correlation coefficient, Spearman, and Kendall rank correlation coefficients, are compared from definition to application domain. Based on the characteristics of the pump's vibration dataset, the nonparametric and distribution‐free Spearman rank correlation coefficient is introduced to analyze the relationship between the pump's working state and each of the 207′880 variables. The percentage of variables and exact variables' tables with high Spearman's correlation coefficients for states I and II, states I and III, states II and III, and three states in different files are obtained respectively, which has important valuation for the future research of the unsupervised machine learning system. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we investigate a typical clustering technology, namely, Gaussian mixture model (GMM)-based approach, for user interest prediction in social networks. The establishment of the model follows the following process: collect dataset from 4613 users and more than 16 million messages from Sina Weibo, obtain each user's interest eigenvalue sequence and establish GMM model to clustering users. In theory and experiment, this approach is feasible. The GMM-based approach considers the prediction accuracy and consuming time. A series of experiments are conducted to validate the feasibility and efficiency of the proposed solution and whether it can achieve a higher accuracy of prediction compared with other approaches, such as SVM and K-means. Further experiments show that GMM-based approach could produce higher prediction accuracy of 93.9%, thus leveraging computation complexity.

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

Social network has become a very popular way for internet users to communicate and interact online. Users spend plenty of time on famous social networks (e.g., Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behavior (e.g., post messages containing commercial URLs, following a larger amount of users, etc.), leading to great misunderstanding and inconvenience on users׳ social activities. In this paper, a supervised machine learning based solution is proposed for an effective spammer detection. The main procedure of the work is: first, collect a dataset from Sina Weibo including 30,116 users and more than 16 million messages. Then, construct a labeled dataset of users and manually classify users into spammers and non-spammers. Afterwards, extract a set of feature from message content and users׳ social behavior, and apply into SVM (Support Vector Machines) based spammer detection algorithm. The experiment shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non-spammers reaching 99.1% and 99.9% respectively.

In recent years, the amount of time series data generated in different domains have grown consistently. Analyzing large time-series datasets coming from sensor networks, power grids, stock exchanges, social networks and cloud monitoring logs at a massive scale is one of the biggest challenges that data scientists are facing. Big data storage and processing frameworks provides an environment to handle the volume, velocity and frequency attributes associated with time-series data. We propose an efficient and distributed computing framework - R2Time for processing such data in the Hadoop environment. It integrates R with a distributed time-series database (Open TSDB) using a MapReduce programming framework (RHIPE). R2Time allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment.

This work is based on a real-life data-set collected from sensors that monitor drilling processes and equipment in an oil and gas company. The sensor data stream-in at an interval of one second, which is equivalent to 86400 rows of data per day. After studying state-of-the-art Big Data analytics tools including Mahout, RHadoop and Spark, we chose Ox data's H2O for this particular problem because of its fast in-memory processing, strong machine learning engine, and ease of use. Accurate predictive analytics of big sensor data can be used to estimate missed values, or to replace incorrect readings due malfunctioning sensors or broken communication channel. It can also be used to anticipate situations that help in various decision makings, including maintenance planning and operation.

With the wide adoption of cloud computing, the scientific applications are migrated to cloud for execution. The complex structure of scientific applications bring challenges to optimization scheduling scientific applications across multiple heterogeneous clouds. In this paper, the directed acyclic graphs (DAGs) are adopted to represent scientific applications, which have different priorities in the process of scheduling. We propose a dynamic multi-cloud priority list scheduling algorithm (DMPLS), combining with workloads preemptive strategy and feedback mechanism to schedule scientific applications in time. Our algorithm regulates the workloads scheduling dynamically based on the updated information about the actual workloads execution time. The experimental results show that the proposed algorithm reduces the average time to complete the applications compared with First-Come-First-Service and Round-Robin algorithm. Moreover, the advantage of the DMPLS algorithm is more significant under the severe resources confliction situations.

Aging-in-Place solutions are becoming increasingly prevalent in our society. New age big data technologies can harness upon enormous amount of data generated from sensors in smart homes to provide enabling services. Added care and preventive services can be furnished through interoperability and bidirectional dataflow across the value chain. However the nature of the problem domain which although allows establishing better care through sharing of information also risks disclosing complete living behavior of individuals. In this paper, we introduce and evaluate a novel scalable k-anonymization solution based upon the distributed map-reduce paradigm for preserving privacy of the shared data in a welfare intercloud. Our evaluation benchmarks both information loss and data quality metrics and demonstrates better scalability/performance than any other available solutions.

Current virtual networks offered by IaaS cloud providers are not under complete control of their tenants. The virtual network configuration is carried out by the service provider, and the functionality is limited by the provider's offerings. This paper presents a new approach for building and maintaining tenant-programmable virtual networks. This type of virtual networks are the basic building blocks for network function virtualization, and significantly facilitates the implementation of network functions in software. Our approach gives tenants complete control over provisioned virtual network components, and simplifies the integration with on-premises resources. The implementation confirmed the practicality and scalability of the solution, at the cost of a small overhead.

The adaptation of new technologies into the electrical energy infrastructure enables development of novel energy efficiency services. Introduction of smart meters into residential households allows collection of granular energy usage measures at frequent intervals. Analysis of such data could bring ample and detailed insights into the consumption behavior of households, allowing more accurate prediction of future loads. With the data intensive nature of these technologies, recent big data solutions allows harnessing of the enormous amounts of data being generated. We present a novel, scalable, distributed gaussian mean clustering algorithm for analyzing the energy consumption behavior of households in relation to different contributing factors such as weather conditions, type of day and time of the day. Based on forecasts of such contributing factors, we were able to predict a household's future energy usage much more accurately than other standard regression methods used for load forecasting.

The paper introduces a new secure power aware many-to-many routing protocol for wireless sensor and actuator networks. Actuators register for sensing data and sensor nodes send data directly to actuators using the proposed protocol. The protocol has two versions. The first version is designed for networks where every node transmits at the same power level. The second version is for the case where nodes can individually adjust the transmission power according to the channel conditions and communications distance. The protocol increases energy efficiency and prolongs network life-time while still keeps the communications secure. Copyright © 2012 John Wiley & Sons, Ltd.

IaaS encourages pooled resource management model, which provides transparency on the management and provision of IT resources. The transparency, hiding physical details of the underlying resources, makes it difficult for cloud users/services to identify trusted resources for service deployment, resulting in potential risks of deploying critical services on untrusted resources. This paper proposes a pool oriented trust management mechanism for cloud infrastructures, allowing the construction and identification of trusted clusters consisted of trusted resources, with strict membership management to accept only trusted physical resources. Resources of a trusted cluster expose identical trust properties/attributes to cloud users, enabling users to verify the trust on the resources without the need of identifying individual physical resource. Hence, service deployment and migration can be augmented with the above trust verification to ensure that services are always deployed on trusted resources.

The vast amounts of data generated from sensors in smart homes, can give valuable insights about social and behavioral patters on households and their residents. The goal of the project is investigation & implementation of mechanisms to capture/store vast continuous streams of time-series data from optical movement sensors, analyze & mine for anomalies/changes enabling preventive care with mechanisms for presentation/visualization of meaningful information to target user groups (next of kin, care providers, professional services), while ensuring that the privacy of participants are preserved.

The tremendous growth in data volumes has created a need for new tools and algorithms to quickly analyze large datasets. Cluster analysis techniques, such as K-means can be used for large datasets distributed across several machines. The accuracy of K-means depends on the selection of seed centroids during initialization. K-means++ improves on the K-means seeder, but suffers from problems when it is applied to large datasets: (a) the random algorithm it employs can produce inconsistent results across several analysis runs under the same initial conditions; and (b) it scales poorly for large datasets. In this paper we describe a new Competitive K-means algorithm we developed that addresses both of these problems. We describe an efficient MapReduce implementation of our new Competitive K-means algorithm that we found scales well with large datasets. We compared the performance of our new algorithm with three existing cluster analysis algorithms and found that our new algorithm improves cluster analysis accuracy and decreases variance. Our results show that our new algorithm produced a speedup of 76 ± 9 times compared with the serial K-means++ and is as fast as the Streaming K-means. Our work provides a method to select a good initial seeding in less time, facilitating accurate cluster analysis over large datasets in shorter time.

The proper operation and maintenance of a network requires a reliable and efficient monitoring mechanism. The mechanism should handle large amount of monitoring data which are generated by different protocols. In addition, the requirements (e.g. response time, accuracy) imposed by long-term planned queries and short-term ad-hoc queries should be satisfied for multi-tenant computing models. This paper proposes a novel mechanism for scalable storage and real-time processing of monitoring data. This mechanism takes advantage of a data-intensive framework for collecting network flow information records, as well as data points' indexes. The design is not limited to a particular monitoring protocol, since it employs a generic structure for data handling. Thus, it's applicable to a wide variety of monitoring solutions.

A new scheme of service oriented architecture for federating services provided by multiple clouds as one application is introduced. Service federating inter-cloud application configuration requires selecting a set of services from various clouds such that the user preferences and constraints are satisfied. This configuration process is defined as a multi criteria design problem with posterior articulation of preferences. An efficient scheme for the configuration of a service federating inter-cloud application is also presented. The experimental results prove the feasibility of our new scheme.

The amount of resources needed to provision Virtual Machines (VM) in a cloud computing systems to support virtual HPC clusters can be predicted from the analysis of historic use data. In previous work, Hacker et al. found that cluster analysis is a useful tool to understand the underlying spatio-temporal dependencies present in system fault and use logs. However, the cluster analysis used for reducing spatio-temporal dependences should be fast and accurate to understand the underlying stochastic properties of these systems. K-means is a fast cluster analysis method, in which accuracy depends on the use of initialization algorithms that are usually serial and slow. In this paper we present two new parallel strategies for fast seeding K-means cluster analysis. Both strategies were tested on a real problem where the aim was to reduce spatial and temporal dependencies of failures on large supercomputer systems. The performance of both strategies were compared with five existing serial implementations: K-means implementations of 1) Lloyd (L); 2) McQueen (M); and 3) Hartigan - Wong (HW), all of them using Forgy seeding; 4) K-means++; and 5) Neural Gas clustering (NG), a more recent and sophisticated method. Our results show that our new Parallel Competitive Fitness approach reduces the Within Sum of Squares (WSQQ) measure, thus increasing cluster quality of the three K-means implementations: L; M; HW, and is 200 times faster than the existing serial K-means++. The existing serial and our new Parallel K-means++ have the lowest WSQQ. Our new Parallel K-means++ is twice as fast as the existing serial K-means++ method, and is 4 times faster than the NG method. Moreover, our new methods did not generate empty clusters, while NG did. As a result of our new techniques, predicting the amount of resources needed to provision VMs processing historic system fault and use data can now be done faster and with more accuracy.

The estimation of the shortest path between two vertices in a network graphs is an important issue for many applications in the real world, which may be road networks, social collaboration networks, biological networks and so forth. The short response time, the less space cost and the high accuracy are three critical evaluation metrics in the approximate calculation of the shortest path. To achieve a quick and accurate calculation of an approximate shortest path distance, the typical method that takes a non-trivial approach calculates the distance between every pair of vertices in advance and records the calculation results with an n×n matrix, where n is the number of vertexes in the network graph. Unfortunately, it hard for this method function because it is difficult to sustain the huge amount of storage space and time consuming preprocessing for the big size of the network graph in practice. Unlike many efforts that have been made to minimize the costs of time and space in enhancing the accuracy of the shortest path calculation, this paper proposes an estimation scheme of the shortest path calculation for power-law graphs, due to the fact that many networks in the real applications are power-law networks. The theoretical analysis indicates that the schema is successfully optimized so that the query time can be an approximation constant and the calculation result of the approximate shortest path distance is almost 2 to 3 times as long as the actual shortest path distance between a pair of vertices. A simulation experiment also validates the analysis and demonstrates the feasibility of the proposed scheme.

Cloud and IT service providers should act as responsible stewards for the data of their customers and users. However, the current absence of accountability frameworks for distributed IT services makes it difficult for users to understand, influence and determine how their service providers honour their obligations. The A4Cloud project will create solutions to support users in deciding and tracking how their data is used by cloud service providers. By combining methods of risk analysis, policy enforcement, monitoring and compliance auditing with tailored IT mechanisms for security, assurance and redress, A4Cloud aims to extend accountability across entire cloud service value chains, covering personal and business sensitive information in the cloud.

Due to limitations of power, computation capability and storage resources, wireless sensor networks are vulnerable to many attacks. The paper proposed a novel routing protocol algorithm for Wireless sensor network. The proposed routing protocol algorithm can adopt suitable routing technology for the nodes according to distance of nodes to the base station, density of nodes distribution and residual energy of nodes. Comparing the proposed routing protocol algorithm with other routing protocol algorithm through comprehensive analysis, the results show that the proposed routing protocol algorithm is secure and efficient for wireless sensor networks.

Providing consistent security services in on-demand provisioned Cloud infrastructure services is of primary importance due to multi-tenant and potentially multi-provider nature of Clouds Infrastructure as a Service (IaaS) environment. Cloud security infrastructure should address two aspects of the IaaS operation and dynamic security services provisioning: (1) provide security infrastructure for secure Cloud IaaS operation, (2) provisioning dynamic security services, including creation and management of the dynamic security associations, as a part of the provisioned composite services or virtual infrastructures. The first task is a traditional task in security engineering, while dynamic provisioning of managed security services in virtualised environment remains a problem and requires additional research. In this paper we discuss both aspects of the Cloud Security and provide suggestions about required security mechanisms for secure data management in dynamically provisioned Cloud infrastructures. The paper refers to the architectural framework for on-demand infrastructure services provisioning, being developed by authors, that provides a basis for defining the proposed Cloud Security Infrastructure. The proposed SLA management solution is based on the WS-Agreement and allows dynamic SLA management during the whole provisioned services lifecycle. The paper discusses conceptual issues, basic requirements and practical suggestions for dynamically provisioned access control infrastructure (DACI). The paper proposes the security mechanisms that are required for consistent DACI operation, in particular security tokens used for access control, policy enforcement and authorisation session context exchange between provisioned infrastructure services and Cloud provider services. The suggested implementation is based on the GAAA Toolkit Java library developed by authors that is extended with the proposed Common Security Services Interface (CSSI) and additional mechanisms for binding sessions and security context between provisioned services and virtualised platform.

This paper compares k-means and fuzzy c-means for clustering a noisy realistic and big dataset. We made the comparison using a free cloud computing solution Apache Mahout/ Hadoop and Wikipedia's latest articles. In the past the usage of these two algorithms was restricted to small datasets. As so, studies were based on artificial datasets that do not represent a real document clustering situation. With this ongoing research we found that in a noisy dataset, fuzzy c-means can lead to worse cluster quality than k-means. The convergence speed of k-means is not always faster. We found as well that Mahout is a promise clustering technology but the preprocessing tools are not developed enough for an efficient dimensionality reduction. From our experience the use of the Apache Mahout is premature.

Task consolidation is a way of maximizing cloud computing resource, which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy usage. Many literature show that energy consumption and resource utilization in clouds are highly coupled. Some research works aim to decrease resource utilization for saving energy while some try to find the balance between resource utilization and energy consumption. In this paper, an energy-aware task consolidation (ETC) technique is presented aims to optimize energy consumption of virtual clusters in cloud data center. Conforming most cloud systems, a 70% principle of CPU utilization is proposed to manage task consolidation among virtual clusters. The simulation results show that ETC can significantly reduce power consumption in managing task consolidation for cloud systems. Up to 17% improvement as compare to a recent work in [10] that aims to maximize resource utilization can be obtained.

Integer factorization is an interesting but a hard problem and stays at the core of many security mechanisms. Conventional approaches to factor big integer numbers often require powerful computers and a great effort in software development. In this paper, we present a different approach to this problem by running the quadratic sieve algorithm in the Hadoop framework. This approach offers a much easier way to develop program and to setup the working environment. It can also be scaled easily to work with a large number of computers.

In this paper we survey state-of-the-art of integration and application of Cloud Computing (CC) to High Performance Computing (HPC). Motivation and general application areas are presented demonstrating particular focus on commoditization of HPC resources. Current experiments usually show significant performance differences between CC and HPC infrastructures and also programming models. However, recent research efforts aim at finding a common ground between those approaches. A conclusion emerges that some level of synthesis of CC and HPC is inevitable and probably beneficial for both, however, it requires further significant research efforts.

Cloud computing is emerging as a serious paradigm shift in the way we use computers. It relies on several technologies that are not new. However, the increasing availability of bandwidth allows new combinations and opens new IT perspectives. The data storage and processing power are being moved to more efficient and centralized structures over the web. Costs are being reduced with the loss of our data control as a trade-off. It will almost be inevitable for companies not to follow this trend. Yet, there are some important challenges to overcome. This paper discusses Cloud Computing concept concerning privacy and how it may affect our freedom of speech.

Data have become more and more important these years, especially for big companies, and it is of great benefit to mine useful information in these data. Oil & Gas industry has to deal with vast amounts of data, both in real-time and historical context. As the amount of data is significant, it is usually infeasible or very time consuming to actually process the data. In our project we investigate usage of Hadoop to solve this problem. In order to perform Hadoop jobs, data must first exist in the Hadoop file system, which creates the problem of data acquisition. In this paper, two solutions are investigates, performance comparison is performed and solution based on Chukwa is demonstrated to be more efficient than a naïve implementation in particular for bigger file sizes.

Cloud computing is an emerging computing paradigm in which IT resources and capacities are provided as services over the Internet. Promising as it is, this paradigm also brings forth new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are likely outside of the same trust domain of data owners. To maintain the confidentiality of, sensitive user data against untrusted servers, existing work usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. In this paper, we present a way to implement, scalable and fine-grained access control systems based on attribute-based encryption (ABE). For the purpose of secure access control in cloud computing, the prevention of illegal key sharing among colluding users is missing from the existing access control systems based on ABE. This paper addresses this challenging open issue by defining and enforcing access policies based on data attributes and implementing user accountability by using traitor tracing. Furthermore, both the user grant and revocation are efficiently supported by using the broadcast encryption technique. Extensive analysis shows that the proposed scheme is highly efficient and provably secure under existing security models.

This paper establishes a relation between Cloud Computing and Cyber Physical Space. It demonstrates how Cloud Computing technologies provide necessary technical means to create Cyber Physical Space. At the same time it describes examples of cloud services that already create elements of the Cyber Physical Space., This relation is subsequently used as an important basis for analysis of sustainability impacts of Cloud Computing on human in society and nature. Due to significant growth of Cloud Computing a deep and critical discussion of those impacts is important. The main findings suggest that society is not equipped with right sociological and technical tools to handle the impacts of this rapid change, and that responsibility lays in the design that will ensure the sustainability through properly modeled interactions in Cyber Physical Space.

Cloud computing has been acknowledged as one of the prevaling models for providing IT capacities. The off-premises computing paradigm that comes with cloud computing has incurred great concerns on the security of data, especially the integrity and confidentiality of data, as cloud service providers may have complete control on the computing infrastructure that underpins the services. This makes it difficult to share data via cloud providers where data should be confidential to the providers and only authorized users should be allowed to access the data. This work aims to construct a system for trusted data sharing through untrusted cloud providers, to address the above mentioned issue. The constructed system can imperatively impose the access control policies of data owners, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data.

Cloud-based systems have proven to be a powerful technology for building data-intensive applications. However, the process of designing and deploying such applications is still primarily a manual one. There is a need for mechanisms and tools to help automate the required development steps. Using the Semantic Web ontology language OWL and the Hadoop platform we have developed a number of models and associated software tools that provide an end-to-end solution for designing and deploying cloud-based systems. This solution supports the construction of detailed models of data dependencies and their validation. It also enables generation and deployment of cloud-based data flows from those models. We illustrate its use for detecting alarm scenarios using data from vast underwater sensor-network.

A new secure power aware many-to-many routing protocol for wireless sensor and actuator networks is introduced. The protocol has two versions. The first version is designed for networks where every node transmits at the same power level. The second version is for the case where nodes can individually adjust the transmission power according to the channel conditions and communications distance. In both versions, actuators are registered for sensed data, and every intermediate node that relays registration messages authenticates them through an efficient and secure protocol. When sensor nodes have data, they are sent directly to the actuators registered for that type of data by using a secure many to many routing protocol.

An Ad hoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure, so they are vulnerable to many attacks and the security of the network can not be ensued. In this paper, we present a novel Intrusion Detection Mechanism based on the Trust Model (IDMTM) for mobile Ad hoc networks. In IDMTM, we employ two new concepts: "Evidence Chain (EC)" and "Trust Fluctuation (TF)" to accurately evaluate the trust value of a node in the network for judging whether it is malicious or not. Comparing with other Intrusion Detection System, IDMTM can greatly decrease the possibility of false-alarm with by efficiently utilizing the information collected from the local node and the neighboring nodes. Also, IDMTM can efficiently isolate internal malicious nodes from the networks and enhance the security without compromising the performance of the networks.

Computer Crime Surveys are important inputs to management and authorities, providing information on the national IT security status. Such measurement instruments are increasingly valuable as more and more enterprises become critically dependent on IT and the Internet. The article presents a selecti on of findings from the Norwegian Computer Crime and Security Survey 2006 and discusses strengths and weaknesses of the survey. The survey reveals that next to malware infection and theft of IT equipment, hacking is the most commonly reported computer crime incident. The findings also document that there are large differences in security practices between large and small enterprises, even when it comes to measures one would have thought that all enterprises independent of size would have implemented. This practice may put small enterprises in a position of high risk. This is also worrying in a national context as small enterprises make up the majority of the total number of enterprises. Similar to previous surveys, the 2006 survey shows that the number of reported computer crime incidents is low because of weak detection mechanisms. Finally, a SWOT analysis of the 2006 survey is conducted to review improvements of the survey as a measurement tool.

Protection of the network layer in mobile ad hoc networks (MANETs) imply cryptographically signed routing messages. Identity-based signature (IBS) schemes make bandwidth consuming certificate exchanges obsolete. User identifiers serve as public keys. However, long-term identifiers are required. This paper analyzes the applicability of IBS schemes for securing routing information in MANETs for emergency and rescue operations. The Optimized Link State Routing protocol (OLSR) serves as example.

Grid is a distributed computing and resource environment. Security is an important issue in the grid environment. In this paper, we present a prototype of the grid security architecture. It shows that the architecture is scalable, and meets the security requirements of the grid.

A power mapping f(x)=x/sup d/ over GF(p/sup n/) is said to be differentially k-uniform if k is the maximum number of solutions x/spl isin/GF(p/sup n/) of f(x+a)-f(x)=b where a, b/spl isin/GF(p/sup n/) and a/spl ne/0. A 2-uniform mapping is called almost perfect nonlinear (APN). We construct several new infinite families of nonbinary APN power mappings.

The quaternary Calderbank-McGuire (see Des., Codes Cryptogr., vol.10, no.2, 1997) code is a Z/sub 4/-linear code of length 32 which has 2/sup 37/ codewords and a minimum Lee distance of 12. The Gray map of this code is known to be a nonlinear binary (64, 2/sup 37/,12) code. The Z/sub 4/-linear Calderbank-McGuire code can correct all errors with Lee weight /spl les/5. An algebraic decoding algorithm for the code is presented in this paper. Furthermore, we discuss an alternative decoding method which takes advantage of the efficient BCH decoding algorithm.