Deep learning (DL)-based channel estimation models are capable of processing large-scale wireless data and have shown strong performance in modeling complex reconfigurable intelligent surface (RIS) channels. However, their vulnerability to adversarial attacks remains underexplored. To address this gap, this paper analyzes vulnerabilities in DL-based channel estimation models for RIS-assisted communications. We propose a novel adaptive adversarial training framework based on projected gradient descent (PGD) as an effective defense strategy learning from large-scale data. Unlike standard adversarial training, the proposed framework employs a progressive adversarial schedule that incrementally increases both perturbation strength and PGD iteration-depth during training. In addition, a balanced composition of clean and adversarial samples is maintained within each mini-batch, preserving baseline accuracy while systematically enhancing robustness against strong adversarial attacks. Extensive simulations are conducted to evaluate our proposed strategy compared with other existing defense techniques. The results show that our approach significantly enhances the robustness of DL-based channel estimation models against adversarial attacks.

Wireless-powered multi-access edge computing (WP-MEC), as a promising computing paradigm with the great potential for breaking through the power limitations of wireless devices, is facing the challenges of reliable task offloading and charging power allocation. Towards this end, we formulate a joint optimization problem of wireless charging and computation offloading in socially-aware D2D-assisted WP-MEC to maximize the utility, characterized by wireless devices’ residual energy and the strength of social relationship. To address this problem, we propose a deep reinforcement learning (DRL)-based approach with hybrid actor-critic networks including three actor networks and one critic network as well as with Proximal Policy Optimization (PPO) updating policy. Further, to prevent the policy collapse, we adopt the PPO-clip algorithm which limits the update steps to enhance the stability of algorithm. The experimental results show that the proposed algorithm can achieved superior convergence performance and, meanwhile, improves the average utility efficiently compared to other baseline approaches.

Deep reinforcement learning (DRL) has achieved remarkable success in solving sequential decision-making problems across various domains. However, a critical challenge is sample inefficiency, especially in real-world environments with high-dimensional solution spaces due to continuous state and action spaces. Although off-policy actor-critic algorithms have been proposed to mitigate this issue, the gains in sample efficiency remain limited, as decision-making in these algorithms relies solely on the policy function that might not always yield optimal actions. To bridge the gap, we design a novel feedback-based decision-making mechanism (FADA) that incorporates a feedback mechanism into the actor-critic framework to enhance decision-making robustness. Specifically, FADA utilizes feedback from the value function (critic) to calibrate the decisions produced by the policy function (actor). More concretely, FADA comprises four integrated modules: a decision-space expansion module (DEM) to produce a pool of candidate actions, a critic-guided evaluation module (CGEM) that estimates the efficacy of the candidate actions, an adaptive selection module (ASM) that adaptively selects a set of elite actions based on the estimated efficacy and samples the final action, and an iterative refinement module (IRM) that improves the quality of elite actions. We evaluate our approach on multiple tasks in the DeepMind Control Suite, and the experimental results demonstrate a significant improvement in sample efficiency.

In model-based reinforcement learning (MBRL), the quality of simulated experiences is a critical bottleneck to effective policy learning. Existing research has primarily focused on reducing the generation errors of these simulated experiences but has largely ignored how the varying quality of these experiences impacts policy learning during their exploitation. To bridge this gap, we propose a novel quality-aware experience exploitation scheme, called QA2E, which dynamically exploits simulated experiences based on their assessed quality to enhance the effectiveness of model-based policy learning. Particularly, we develop a weighted Bellman backup approach to dynamically adjust the influence of simulated experiences on policy learning based on their assessed quality. Since directly measuring the quality is impractical, QA2E estimates it through the epistemic uncertainty derived from the prediction results of an ensemble of transition models. Experimental results demonstrate that QA2E significantly improves policy learning performance by more effectively exploiting simulated experiences.

Secure inference in a deep-learning-as-a-service setting (DLaaS) can effectively protect sensitive data of the client and server model parameters. However, various nonlinear computations heavily hinder its efficiency. To address this issue, we propose a secure three-party inference framework, called LSTN, to ensure the privacy of client input data and meanwhile achieve prediction accuracy close to the plaintext setting. Specifically, we leverage replicated secret sharing to design a novel secure three-party comparison protocol that will be employed to develop a secure ReLU function. Our developed protocol can achieve high communication efficiency in the scenario of having a majority of honest parties. The experimental result shows that the inference time is 6× faster than the prevailing computing framework, CrypTen.

Artificial Intelligence of Things (AIoT) represents a collaborative fusion of artificial intelligence (AI) and the Internet of Things (IoT). AIoT systems enable real-time data acquisition through IoT sensors and conduct intelligent data analysis tasks across the entire spectrum from terminal to edge to cloud, creating a dynamic and empowering ecosystem. However, the evolving landscape of AIoT faces a perplexing challenge: how to effectively sense the geographically diverse and highly variable environment, accurately gather massive and diverse IoT data with varying value density, and intelligently integrate multisource data to deliver real-time, intelligent, and high-quality applications.

Well-trained neural network models are deployed on edge servers to provide valuable inference services for clients. To protect data privacy, a promising way is to exploit various types of secret sharing to implement privacy-preserving neural network inference. However, existing schemes suffer high communication rounds and overhead, making them hardly practical. In this paper, we propose Cenia, a new communication-efficient privacy-preserving neural network inference model. Specifically, we exploit arithmetic secret sharing to develop low-interaction secure comparison protocols, that can be used to realize secure activation layers (e.g., ReLU) and secure pooling layers (e.g., max pooling) without expensive garbled circuit and oblivious transfer primitives. Besides, we also design secure exponent and division protocols to realize secure normalization layers (e.g., Sigmoid). Theoretical analysis demonstrates the security and low complexity of Cenia. Extensive experiments have also been conducted on benchmark datasets and classical models, and experimental results show that Cenia achieves privacy-preserving, accurate, and efficient neural network inference. Particularly, Cenia can achieve 37.5% and 60.76% of Sonic’s communication rounds and overhead, respectively, compared to Sonic (i.e., the state-of-the-art scheme).

Earth observation applications, such as emergency surveillance and disaster relief, are thriving due to the availability of earth observation satellites that provide timely and objective observation data at different spatial and temporal scales. Such observation data is processed at the satellite edge with orbital edge computing, leading to potentially reduced bandwidth cost and transmission delay. However, most existing studies primarily focus on optimizing computation offloading but ignore the consideration of object observation and observation data transmission. To fill this gap, this paper proposes a novel scheduling approach that jointly considers observation satellites, relay satellites, and computing satellites in LEO satellite systems, aiming to maximize the number of completed observation tasks while taking into account various requirements of observation, transmission, and computation resources. Specifically, we first formulate the problem of jointly scheduling observation, relay, and computing satellites to maximize the number of accomplished observation tasks. Then, we decompose the formulated problem into two sub-problems and design a resource-aware algorithm called ORCA to determine the optimal scheduling of observation, relay, and computing satellites. Simulation results demonstrate that ORCA outperforms existing algorithms in terms of completing a number of observation tasks.

Green unmanned aerial relaying (UAR) systems, featuring solar-powered unmanned aerial vehicles (UAVs) to provide agile and flexible communication services, have recently gained significant attention for their wide applications. Nonetheless, due to the essential dynamics of wireless channel conditions and solar energy supply, maintaining system stability is a critical concern in practical green UAR systems, as fluctuation in these two factors can significantly affect communication performance. To address this issue, this article proposes a scheme design for stable communications in a UAR system with dynamic solar energy supply and wireless channel conditions. Specifically, we first formulate an optimization problem, called OFL, to control the power allocation and flow data rate assignment to minimize long-term time-averaged energy consumption while ensuring the demanded data rate and system stability. Considering that solving OFL needs complete prior information about the solar power supply and wireless channel conditions, which is hardly acquired, we then explore the Lyapunov theory to transform OFL as an online optimization problem, called ONL. To find the solution to ONL, we subsequently design a distributed algorithm that enables each UAV to make its own decision locally, thus, reducing the computational overhead of each UAV. Particularly, we prove that stability can be guaranteed by employing the designed algorithm. Extensive simulations are conducted to show the performance achieved by the proposed scheme.

In this article, we study a problem of improving the communication performance in a solar-assisted unmanned aerial vehicle (UAV) communication system (SA-UCS) where UAVs equipped with solar panels are deployed to provide communication services to ground users in a target area. Previous works have ignored the inherent uncertainty in solar energy supply caused by naturally unpredictable weather conditions and UAV vibrations, consequently degrading communication performance in SA-UCSs. To bridge the gap, we propose to explore the extreme value theory to tackle uncertain solar energy supply. Specifically, we formulate a throughput maximization problem that is subject to transmission power, link capacity, UAV position, and minimum residual energy. To handle the uncertainty in solar energy supply, we propose to find the generalized extreme value (GEV) distribution of the minimum amount of harvested solar energy, in order to bound the solar energy supply in extreme cases. To solve the formulated problem, which is in general nonconvex, we develop an iterative optimization method that first decomposes the originally formulated problem into two subproblems being solved alternately at each iteration through employing the successive convex approximation (SCA) technique. We perform extensive simulations using real-world data from the National Solar Radiation Database. The simulation results corroborate the throughput enhancement with guaranteed energy supply available for UAV communications. Additionally, the results also show the fast convergence achieved by the developed iterative method.

Unmanned Aerial wireless Communication Systems (UASs), featuring the low cost and flexible deployment of unmanned aerial vehicles (UAVs), have attracted intensive attention recently to provide wireless communication services in some specific scenarios, e.g., disaster areas and temporary hotspots. Nonetheless, due to UAVs’ limited on-board energy storage, the provisioning of wireless communications can deplete their carried energy, consequently landing on the ground. To mitigate this issue, harvesting solar energy to power UAVs is a promising alternative solution. However, the essential dynamics of solar energy can seriously affect communication performance in UASs. In this paper, we explore dynamic solar energy to supply a UAS with an aerial base station (BS) and aim to minimize the long-term time-averaged energy consumption of the UAS. Particularly, we formulate a Long-term time-averaged Energy Consumption minimization problem (LEC) by jointly taking into account transmission power and data rate. Considering that LEC is time-coupling nonlinear programming (NLP), we reformulate a relaxed online optimization problem, called STP (single-time slot problem), by employing Lyapunov optimization theory. Then, we develop a joint power and rate control algorithm to solve STP. Particularly, we theoretically show that the proposed algorithm can achieve (D/V + C)-approximation and guarantee stability. Extensive simulation results have shown the performance gain, in terms of stability and throughput, achieved by the proposed algorithm.

User authentication on the blockchain has been considered a promising solution to secure communications in LEO satellite communication systems. Due to resource-limited LEO satellites, the blockchain needs to be deployed in the terrestrial network component of LEO satellite communication systems, consequently resulting in high authentication delays. To fill the gap, we propose to cache the blockchain at LEO satellites and update the blockchain periodically and design a delay-aware cooperative caching scheme for on-chain authentication by considering the query delay and the synchronization delay. Specifically, we first propose to divide LEO satellites into multiple clusters which have the same copy of all the blocks belonging to the blockchain. Then, we model the clustering problem as a coalition formation game. Afterward, we design a distributed delay-aware coalition formation algorithm, which is called DAC, to find an optimal coalition partition. Extensive simulation results show the efficacy of the proposed scheme.

Nonlinearly constrained nonlinear programming (NLC-NLP) problems arise in various real-world decision-making fields, such as financial engineering, urban planning, supply chain management, and power system control. They are usually large-scale because of having to consider massive variables and constraints. Solving NLC-NLP problems by employing common algorithms (e.g., gradient projection method (GPM)) is usually computationally-expensive, which challenges common organizations in solving large-scale NLC-NLP problems. To address this issue, an option is to adopt cloud computing for help. However, this raises security concerns since real-world NLC-NLP problems may carry sensitive information. Although previous secure outsourcing algorithms try to protect sensitive information, they still let cloud service tenants bear heavy computation burden. In this paper, we develop a practical secure outsourcing algorithm for using the GPM to solve large-scale NLC-NLP problems. To be more prominent, to accelerate computations and avoid possible memory overflowing, we parallelize the developed algorithm. We implement the developed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop, and also offer extensive experiment results to show that the developed algorithm can reduce the tenant’s computing time significantly.

Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an$\epsilon $-bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than$(1+\epsilon)$of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.

We are now in the big data era. Due to the emerging various systems and applications, such as the Internet of Things, cyber-physical systems, smart cities, smart healthcare, we are able to collect more data than ever before. On the other hand, it makes it very difficult to analyze such massive data in order to advance our science and engineering fields. We note that QR and LU factorizations are two of the most fundamental mathematical tools for data analysis. However, conducting QR or LU factorization of an m×n matrix requires computational complexity ofO(m2n). This incurs a formidable challenge in efficiently analyzing large-scale data sets by normal users or small companies on traditional resource-limited computers. To overcome this limitation, industry and academia propose to employ cloud computing that can offer abundant computing resources. This, however, obviously raises security concerns and hence a lot of users are reluctant to reveal their data to the cloud. To this end, we propose two secure outsourcing algorithms for efficiently performing large-scale QR and LU factorizations, respectively. We implement the proposed algorithms on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.

An unmanned aerial system (UAS), consisting of unmanned aerial vehicles (UAVs) with wireless transceivers, has been considered as an indispensable complement to conventional terrestrial communication infrastructure that cannot fully meet mobile users' demand on ubiquitous connectivity, particularly in some practical areas, such as complex terrains, disaster areas, and temporary traffic hotspots. Thanks to UAVs' high maneuverability and flexible deployment, a UAS can be deployed in these areas to offer ubiquitous connectivity in a timely and cost-effective way. However, due to the limited onboard energy storage capacity, UAVs have to frequently land for energy replenishment, which inevitably affects the provisioning of wireless connectivity services. To prolong UAVs' hovering time, adopting solar energy to power them is an alternative way. In this paper, we explore the joint control of routing, data rate, and transmission power to minimize the energy consumption rate of relaying data in solar-powered UASs. The energy consumption rate minimization problem is formulated as a nonlinear programming (NLP), which is generally NP-hard. To efficiently solve the formulated NLP, we develop an ∊-bounded approximation algorithm that employs a piece-wise linear approximation approach to approximate its nonlinear term and thus reformulate the original problem as classic linear programming (LP). Particularly, we find a theoretical performance gap that is bounded by the approximation error ∊. We conduct extensive simulations to show significant performance improvement.

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.

Community detection is an effective approach to unveil social dynamics among individuals in social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties like advertisement companies, hospitals, with access to social networks for different purposes, which can easily lead to privacy breaches. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topologies and the users’ attributes. In particular, we propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Through both theoretical analysis and experimental validation using synthetic and real world social networks, we demonstrate that the proposed DPCD scheme detects social communities under modest privacy budget.

The past few years have witnessed the explosive growth of mobile user equipment (UEs) and the popularity of computation-intensive applications, leading to a mobile edge computing paradigm for Ultra-dense wireless networks (UDNs). Since numerous UEs need to offload a large amount of computation tasks to edge servers/UEs, it is very challenging to coordinate computation offloading among UEs and edge servers in UDNs. To address this issue, we propose a decentralized computation offloading coordination platform that is based on the blockchain. Specifically, we first establish a blockchain platform for announcing computation offloading requests and coordinate computation offloading among UEs and edge servers. Then, we develop a modified GS-based user matching algorithm to find the matching relationship between offloading requester's computation tasks and the edge server/UEs. In particular, user matching is based on task execution time and energy consumption. We conduct simulations and provide extensive simulation results to show the significant performance improvement achieved by the proposed offloading scheme.

Big data has become a key basis of innovation and intelligence, potentially making our lives more convenient and bringing new opportunities to the modern society. Towards this goal, a critical underlying task is to solve a series of large-scale fundamental problems. Conducting such large-scale data analytics in a timely manner requires a large amount of computing resources, which may not be available for individuals and small companies in practice. By outsourcing their computations to the cloud, clients can solve such problems in a cost-effective way. However, confidential data stored at the cloud is vulnerable to cyber attacks, and thus needs to be protected. Previous works employ cryptographic techniques like homomorphic encryption, which significantly increase the computational complexity of solving a large-scale problem at the cloud and is impractical for big data applications. For the first time in the literature, we present an efficient secure outsourcing scheme for convex separable programming problems (CSPs). In particular, we first develop efficient matrix and vector transformation schemes only based on arithmetic operations that are computationally indistinguishable both in value and in structure under a chosen-plaintext attack (CPA). Then, we design a secure outsourcing scheme in which the client and the cloud collaboratively solve the transformed problems. The client can efficiently verify the correctness of returned results to prevent any malicious behavior of the cloud. Theoretical correctness and privacy analysis together show that the proposed scheme obtains optimal results and that the cloud cannot learn private information from the client's concealed data. We conduct extensive simulations on Amazon Elastic Cloud Computing (EC2) platform and find that our proposed scheme provides significant time savings to the clients.

Inference using deep neural networks may be outsourced to the cloud due to its high computational cost, which, however, raises security concerns. Particularly, the data involved in deep neural networks can be highly sensitive, such as in medical, financial, commercial applications, and hence should be kept private. Besides, the deep neural network models owned by research institutions or commercial companies are their valuable intellectual properties and can contain proprietary information, which should be protected as well. Moreover, an untrusted cloud service provider may return accurate and even erroneous computing results. To address the above issues, we propose a secure outsourcing framework for deep neural network inference called SecureNets, which can preserve both a user’s data privacy and his/her neural network model privacy, and also verify the computation results returned by the cloud. Specifically, we employ a secure matrix transformation scheme in SecureNets to avoid privacy leakage of the data and the model. Meanwhile, we propose a verification method that can efficiently verify the correctness of cloud computing results. Our simulation results on four- and five-layer deep neural networks demonstrate that SecureNets can reduce the processing runtime by up to $64%$. Compared with CryptoNets, one of the previous schemes, SecureNets can increase the throughput by $104.45%$ while reducing the data transmission size by $69.78%$ per instance.

With the onset of the big data era, designing efficient and effective machine learning algorithms to analyze large-scale data is in dire need. In practice, data is typically generated by multiple parties and stored in a geographically distributed manner, which spurs the study of distributed machine learning. Traditional master-worker type of distributed machine learning algorithms assumes a trusted central server and focuses on the privacy issue in linear learning models, while privacy in nonlinear learning models and security issues are not well studied. To address these issues, in this paper, we explore the blockchain technique to propose a decentralized privacy-preserving and secure machine learning system, called LearningChain, by considering a general (linear or nonlinear) learning model and without a trusted central server. Specifically, we design a decentralized Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the blockchain. In decentralized SGD, we develop differential privacy based schemes to protect each party’s data privacy, and propose an l-nearest aggregation algorithm to protect the system from potential Byzantine attacks. We also conduct theoretical analysis on the privacy and security of the proposed LearningChain. Finally, we implement LearningChain on Etheurum and demonstrate its efficiency and effectiveness through extensive experiments.

Tensor decompositions are very powerful tools for analyzing multi-dimensional multi-modal data. Particularly, CP tensor decomposition is one of the most fundamental tensor decomposition models. However, it is usually computationally expensive to conduct CP tensor decompositions on a large-scale tensor by common algorithms like alternative least squares (ALS). To address this issue, one widely recognized solution is to adopt cloud computing. However, this raises privacy concerns due to the private information carried by a tensor. Previous algorithms for privacy-preserving outsourcing of tensor decompositions and other related computations require heavy communication cost. In this paper, we first develop an efficient tensor transformation scheme to protect the private information carried by elements' values of a tensor. Then we design a privacy-preserving outsourcing algorithm for ALS based CP tensor decompositions. We implement our proposed algorithm on a laptop and Amazon EC2 cloud and offer experiment results to show the sianificant computing time-savings.

The popularity of smart mobile devices has resulted in the surged growth of mobile data traffic, which makes current cellular communication systems overloaded. To accommodate the data, the current wireless communication system is evolving to a 5G wireless communication system that employs multiple technologies to boost its system capacity. We notice that non-line-of-sight (NLOS) transmission is ubiquitous in wireless communication systems, and is even more common in 5G wireless communication systems due to using millimeter-Wave (mmWave) communications. Previous works employ beamforming techniques to enhance NLOS transmission performance but suffer from the high cost for controlling antennas. In this paper, we propose a dynamic transmission power control scheme for improving NLOS transmission performance. Particularly, we explore the control of UE association with MBS/SBSs and power allocation to maximize UEs' sum-rate under the constraints of transmission power and UEs' quality of service (QoS). To solve this maximization problem, we propose a deep Q- network (DQN) scheme, in which we apply a convolutional neural network (CNN) to estimate the Q-function offline and conduct a deep Q-learning online to find the control strategy. We offer simulation results to show the efficacy of the proposed scheme.

Due to the rapid growth of various systems and applications like cyber-physical systems, smart cities, and e-commerce systems, we have a massive volume of data collected from these systems and applications. We notice that matrix convolution is one of the most fundamental operations on largescale data, such as using it for image registration and object detection. However, performing large-scale matrix convolutions is usually time-consuming, hence hindering a general-purpose computer to conduct large-scale matrix convolutions by its own. Cloud computing allows using an economical way to offload the most expensive computations to the cloud. This, however, obviously raise security concerns. To this end, we proposed an efficient secure outsourcing scheme for large-scale matrix convolutions. Specifically, the user first masks the matrices for protecting the security and sends the masked matrices to the cloud. Then, the cloud conducts matrix convolution and returns the result to the user. Finally, the user recovers the real result from the returned one. Particularly, the matrix convolution is performed in a non- interactive way, hence leading to the very low communication cost. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.

Solving large-scale sparse linear systems of equations (SLSEs) is one of the most common and fundamental problems in big data, but it is very challenging for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such expensive computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. Specifically, clients’ SLSEs usually contain private information that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of linear systems of equations (LSEs) have high computational complexity, and do not exploit the sparsity in the LSEs. More importantly, they share a common serious problem, i.e., a huge number of memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale SLSEs, which has low computational and memory I/O complexities and can protect clients’ privacy well. We implement our algorithm on Amazon Elastic Compute Cloud, and find that the proposed algorithm offers significant time savings for the client (up to 74 percent) compared to previous algorithms.

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

The massive amount of data that is being collected by today's society has the potential to advance scientific knowledge and boost innovations. However, people often lack sufficient computing resources to analyze their large-scale data in a cost-effective and timely way. Cloud computing offers access to vast computing resources on an on-demand and pay-per-use basis, which is a practical way for people to analyze their huge data sets. However, since their data contain sensitive information that needs to be kept secret for ethical, security, or legal reasons, many people are reluctant to adopt cloud computing. For the first time in the literature, we propose a secure outsourcing algorithm for large-scale quadratic programs (QPs), which is one of the most fundamental problems in data analysis. Specifically, based on simple linear algebra operations, we design a low-complexity QP transformation that protects the private data in a QP. We show that the transformed QP is computationally indistinguishable under a chosen plaintext attack (CPA), i.e., CPA-secure. We then develop a parallel algorithm to solve the transformed QP at the cloud, and efficiently find the solution to the original QP at the user. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop. We find that our proposed algorithm offers significant time savings for the user and is scalable to the size of the QP.

The database-driven cognitive radio network (CRN) is regarded as a promising way for a better utilization of spectrum resources without introducing the interference to primary users (PUs). However, there are some critical security and privacy issues in database-driven CRNs, which have been rarely discussed before. First of all, in order to retrieve the spectrum available information (SAI) of one's vicinity, an SU's query will inevitably disclose its location information. Second, malicious SUs may query SAI for other locations so as to infer operational patterns of PUs and other SUs. In addition, they can reconstruct the entire SAI of the database and sell it for profit. Therefore, in this paper we aim to guarantee both location privacy of SUs and information security of the database during spectrum query in database-driven CRNs. We first leverage private information retrieval (PIR) techniques to allow the database to find out the SAI regarding a querying SU's location, without learning the query information, i.e., this SU's location. To prevent malicious SUs inferring SAI of other locations, SUs are required to provide location proofs indicating that they are at the places where they claim to be. Theoretical analysis is provided showing that our scheme is privacy-preserving and secure. Experiments are also conducted to evaluate the its efficiency.

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. To be more prominent, in many cases, clients's LSEs contain private data that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of LSEs have high computational complexity. More importantly, they share a common serious problem, i.e., a huge number of external memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients' privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms.

The capacity of cognitive radio (CR) systems can be enhanced significantly by deploying relay nodes to exploit the spatial diversity. However, the inevitable imperfect sensing in CR has vital effects on the policy of relay selection, channel access, and power allocation that play pivotal roles in the system capacity. The increase in transmission power can improve the system capacity, but results in high energy consumption, which incurs the increase of carbon emission and network operational cost. Most of the existing schemes for CR systems have not jointly considered the imperfect sensing scenario and the tradeoff between the system capacity and energy consumption. To fill in this gap, this paper proposes an energy-aware centralized relay selection scheme that takes into account the relay selection, channel access, and power allocation jointly in CR with imperfect sensing. Specifically, the CR system is formulated as a partially observable Markov decision process (POMDP) to achieve the goal of balancing the system capacity and energy consumption as well as maximizing the system reward. The optimal policy for relay selection, channel access, and power allocation is then derived by virtue of a dynamic programming approach. A dimension reduction strategy is further applied to reduce its high computation complexity. Extensive simulation experiments and results are presented and analysed to demonstrate the significant performance improvement compared to the existing schemes. The performance results show that the received reward increases more than 50% and the network lifetime increases more than 35%, but the system capacity is reduced less than 6% only.

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.

The increasing demand for wireless services has led to a severe energy consumption problem with the rising of greenhouse gas emission. While the renewable energy can somehow alleviate this problem, the routing, flow rate, and power still have to be well investigated with the objective of minimizing energy consumption in multi-hop energy renewable wireless mesh networks (ER-WMNs). This paper formulates the problem of network-wide energy consumption minimization under the network throughput constraint as a mixed-integer nonlinear programming problem by jointly optimizing routing, rate control, and power allocation. Moreover, the min-max fairness model is applied to address the fairness issue because the uneven routing problem may incur the sharp reduction of network performance in multi-hop ER-WMNs. Due to the high computational complexity of the formulated mathematical programming problem, an energy-aware multi-path routing algorithm (EARA) is also proposed to deal with the joint control of routing, flow rate, and power allocation in practical multi-hop WMNs. To search the optimal routing, it applies a weighted Dijkstra's shortest path algorithm, where the weight is defined as a function of the power consumption and residual energy of a node. Extensive simulation results are presented to show the performance of the proposed schemes and the effects of energy replenishment rate and network throughput on the network lifetime.

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

In cognitive radio cooperative communication (CR-CC) systems, the achievable data rate can be improved by increasing the transmission power. However, the increase in power consumption may cause the interference with primary users and reduce the network lifetime. Most previous work on CR-CC did not take into account the tradeoff between the achievable data rate and network lifetime. To fill this gap, this paper proposes an energy-efficient joint relay selection and power allocation scheme in which the state of a relay is characterized by the channel condition of all related links and its residual energy. The CR-CC system is formulated as a multi-armed restless bandit problem where the optimal policy is decided in a distributed way. The solution to the restless bandit formulation is obtained through a first-order relaxation method and a primal-dual priority-index heuristic, which can reduce dramatically the on-line computation and implementation complexity. According to the obtained index, each relay can determine whether to provide relaying or not and also can control the corresponding transmission power. Extensive simulation experiments are conducted to investigate the effectiveness of the proposed scheme. The results demonstrate that the power consumption is reduced significantly and the network lifetime is increased more than 40%.

In cognitive radio (CR) cooperative networks, the applicable relay scheduling and channel access directly affect the system. However, in practical system, the CR sensor is bound to sensing error. This may lead to low system performance and interfering with primary users. In this paper, we present a learning based centralized scheme for joint relay scheduling and channel access scheme in CR cooperative networks. Specifically, we formulate the joint relay scheduling and channel access problem as a partially observable Markov decision process (POMDP) system, where the most likely channel state is derived by a learning process. The optimal policy is derived by solving the problem. Extensive simulation results show the effectiveness of our proposed scheme.

In this paper, we present a distributed relay selection and power allocation scheme concurrently considering the channel states of all related links and residual energy state of the relay nodes for cooperative transmission in cognitive radio (CR) networks. Specifically, we formulate the CR network with cooperative transmission as a restless bandit system, which has been widely applied in operations research and stochastic control. The channel state and residual energy state are presented by finite state Markov chains. With this stochastic optimization formulation, the optimal policy for relay selection and power allocation is indexable, meaning that the relay with the highest index should be selected. The proposed scheme can achieve the tradeoff between achievable rate and network lifetime. Simulation results are presented to illustrate the performance of the proposed scheme.

In an underlay paradigm based cognitive radio (CR) network, due to the constraint of interference threshold, secondary users always experience low system capacity. In this paper, we propose a distributed scheme to improve the capacity of underlay based CR network where the communication between secondary users is assisted by a relay node. The relay node is characterized by channel states of all related links, and the channel state transitions is presented by finite state Markov channel. In particular, the CR network with cooperative transmission is formulated as a restless bandit system where each potential relay node acts as a bandit. With this stochastic optimization formulation, the optimal policy is determined in a distributed method. Simulation results is presented to illustrate the performance of the proposed scheme.

In cognitive radio (CR) networks, the multichannel access problem is an important problem, which may directly affect user applications. However, most of previous work on this problem focuses on maximizing physical layer throughput, rather than the end-to-end transmission control protocol (TCP) performance. In this paper, we propose an optimal TCP throughput based channel access scheme in CR networks, and the TCP performance is improved from a cross-layer perspective. Specifically, we formulate the channel access process in CR network as a stochastic system. With the stochastic optimization formulation, the optimal channel access policy is indexable, meaning that the channels with highest indices should be selected to transmit TCP traffic. Simulation results show the TCP throughput can be improved substantially compared with the existing approach that maximizes physical layer throughput.

Due to the heterogeneity of radio access technology and service in the 4G heterogeneous wireless access networks, it has been a great challenge on radio resource management. This paper considers a bandwidth allocation approach for multiple services in fourth generation (4G) heterogeneous wireless access networks where a mobile with multi-homing capability will be able to simultaneously connect to several wireless interfaces. In this scheme, a utility function is introduced to estimate the effect of network performance when the network provide bandwidth to a new arrival connection and the bandwidth offered by different wireless access networks is normalized by using their corresponding capacities. And then, based on the concept of network utility, a bandwidth allocation algorithm is proposed to allocate bandwidth to both CBR and VBR connections depending on utility fairness for same type of service not only within a wireless access network but also among different wireless access networks. Simulation results show that our bandwidth allocation algorithm is effective in allocating bandwidth for both CBR and VBR services while keeping connection blocking probability substantially low.