In standard reinforcement learning, since the uncertainty of task objectives is not adequately considered in the policy training, the policy achieves poor generalization for the out-of-distribution (OOD) tasks. Although considerable efforts have been made to enhance the generalization for OOD tasks, most of these methods overlook the structural information of task representations in latent space during the generation of extrapolative data, resulting in biased and blurred data embeddings, which then affect the policy generalization. To address this issue, we propose a context-based meta-reinforcement learning (meta-RL) method, namely latent variable distribution enhancement sampler (LVDES), which enhances the policy generalization on OOD tasks by providing efficient task representation space and accurate augmentation policy training data for OOD tasks. Specifically, the proposed LVDES consists of four modules: a task inference module, a task separation module, a latent enhancement module (LEM), and a policy module. The task inference module is used to identify the task. The task separation module (TSM) learns a representation space with highly structured separability. The LEM generates relevant additional task trajectories for augmenting policy training data. The policy module learns a policy to solve tasks. By using efficient task representation space and augmented trajectory data, the exploration efficiency and generalization of the policy for OOD tasks can be enhanced by our LVDES method. Extensive experiments are conducted to demonstrate the effectiveness of our method in comparison with existing methods on the MuJoCo and Meta-World benchmarks. The experimental results show that the task completion accuracy of our LVDES on OOD tasks is increased by 60.20%, with the average exploration time being reduced by 62.99% in comparison with the most effective current method, which demonstrates that our LVDES can achieve great policy generalization on OOD tasks.

Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in Internet of Things (IoT). When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsourced tasks may contain sensitive user data. In this article, we propose the first privacy-preserving, edge-assisted EVD outsourcing scheme that securely enables users to outsource EVD tasks to edge servers. We design a privacy-preserving matrix transformation method to encode the original data, ensuring that edge servers cannot access users’ private information. Additionally, we design a verification scheme that enables the user to verify the correctness of the results returned by the edge servers. Our protocol supports parallel computation by multiple edge servers, thus enhancing the efficiency of EVD. The feasibility of our proposed scheme is demonstrated through both theoretical and experimental perspectives.

This study explores the metric of group closeness centrality within the framework of social networks, a departure from the traditional analysis focused solely on the significance of individual nodes. Given the intricate dynamics observed in networks governed by various stakeholders, we introduce a framework that preserves privacy through the application of a greedy algorithm. This approach is designed to evaluate the collective influence of groups while ensuring the confidentiality of individual data. Furthermore, we employ Oblivious Random Access Memory (ORAM) [1] within cloud servers to conceal access patterns, thereby enhancing data privacy. Through comprehensive experimentation across three real-world social network datasets within the MP-SPDZ framework [2], dedicated to secure multi-party computation, we demonstrate the efficiency of our proposed methods.

The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted computation scheme for solving the largest eigenvalue and corresponding eigenvector. We propose a privacy-preserving transformation method to protect data privacy and prevent edge servers from retrieving sensitive information. Mean-while, we design a verification scheme to ensure the correctness of the results returned by the edge servers. In addition, we design a distributed parallel computing scheme to ensure the efficiency of edge computation. Through theoretical analysis and simulation experiments, we verify the feasibility and efficiency of our proposed scheme.

Singular Value Decomposition (SVD) is a widely applied foundational decomposition technique; however, its computational demands often exceed the capabilities of Internet of Things (IoT) devices. While leveraging edge servers can alleviate this load, it may introduce potential security vulnerabilities. Current secure outsourcing computation methods designed for cloud environments are challenging to adapt to distributed schemes in edge computing. Our research proposes a novel secure edge-assisted protocol for IoT devices solving SVD, aiming to conceal the Input/Output matrix and balance computational loads across multiple edge servers. The protocol ensures the confidentiality of original matrices and decomposition results, preventing exposure to edge servers. We conduct a comprehensive theoretical analysis of the protocol’s efficiency and security, substantiating its advancements through experiments.

Decision tree classifiers are pervasively applied in a wide range of areas, such as healthcare, credit-risk assessment, spam detection, and many more. To ensure effectiveness and efficiency, clients usually choose to adopt classification services that are offered by model providers. However, the required data interactions in the evaluation process raise privacy concerns for both the provider and the client, indicating an imminent need for private decision tree evaluation (PDTE). Recently, some works, e.g., [1] (ESORICS'19) and [2] (NDSS'21), try to achieve PDTE by secure outsourcing computation. However, to hide the decision tree structure, [1] and [2] require non-complete decision trees to be made complete by padding dummy nodes, which lead to exponential (provider-side and cloud-side) computation and communication complexity in the depth of the decision tree. This is especially impractical for deep but sparse decision trees. In this paper, we propose a secure and efficient outsourced PDTE protocol with a focus on sparse trees. We avoid padding dummy nodes by vector dot products in outsourcing settings. Through experiments, we show the competitive performance of our design. Compared with [2] on Spambase dataset in the cloud-side, we are 486× more communication efficient in offline phase and 15× more communication efficient in online phase.

Generating Adversarial Network (GAN) is a prominent unsupervised learning method that utilizes two competing neural networks to generate realistic data, which has been widely employed in image synthesis and data augmentation. Outsourcing GAN training to cloud servers can significantly reduce the computation load on local devices. Furthermore, in outsourcing settings, training data can be gathered from multiple users, leading to larger amounts of data and, as a result, improved training accuracy. However, outsourcing is associated with privacy risks, as training data often contains sensitive information. To address this problem, we propose SecureGAN, a privacy-preserving framework for GAN that aims to protect the privacy of the training input and output. We implement secure protocols based on replicated secret sharing technology to protect the privacy of the linear and nonlinear layers. We conduct experiments using the MP-SPDZ framework, and the results demonstrate the effectiveness of the proposed protocols.

Vehicular edge networks involve edge servers that are close to mobile devices to provide extra computation resource to complete the computation tasks of mobile devices with low latency and high reliability. Considerable efforts on computation offloading in vehicular edge networks have been developed to reduce the energy consumption and computation latency, in which roadside units (RSUs) are usually considered as the fixed edge servers (FESs). Nonetheless, the computation offloading with considering mobile vehicles as mobile edge servers (MESs) in vehicular edge networks still needs to be further investigated. To this end, in this article, we propose a Deep-Reinforcement-Learning-based computation offloading with mobile vehicles in vehicular edge computing, namely, Deep-Reinforcement-Learning-based computation offloading scheme (DRL-COMV), in which some vehicles (such as autonomous vehicle) are deployed and considered as the MESs that move in vehicular edge networks and cooperate with FESs to provide extra computation resource for mobile devices, in order to assist in completing the computation tasks of these mobile devices with great Quality of Experience (QoE) (i.e., low latency) for mobile devices. Particularly, the computation offloading model with considering both mobile and FESs is conducted to achieve the computation tasks offloading through vehicle-to-vehicle (V2V) communications, and a collaborative route planning is considered for these MESs to move in vehicular edge networks with objective of improving efficiency of computation offloading. Then, a Deep-Reinforcement-Learning approach with designing rational reward function is proposed to determine the effective computation offloading strategies for multiple mobile devices and multiple edge servers with objective of maximizing both QoE (i.e., low latency) for mobile devices. Through performance evaluations, our results show that our proposed DRL-COMV scheme can achieve a great convergence and stability. Additionally, our results also demonstrate that our DRL-COMV scheme also can achieve better both QoE and task offloading requests hit ratio for mobile devices in comparison with existing approaches (i.e., DDPG, IMOPSOQ, and GABDOS).

Devices in the Internet-of-Things (IoT) are networked and perform massive computations to support various social IoT systems. Applications in social IoT systems often involve complicated computations that are out of the computation capacity of some resource-constrained IoT devices. Thus, how to enable resource-constrained IoT devices to accomplish complex computations efficiently and securely is of significant importance. To address this problem, we develop a secure edge-aided computation scheme for the social IoT systems. We scope the framework of edge-aided computations and identify the security threats in such a system. We define the security requirements that the outsourcing algorithms should meet. Then, we provide two examples of secure outsourcing algorithms (matrix multiplication and modular exponentiation) that meet the given security requirements. The efficiency and security of the proposed algorithms are supported through the theoretical analysis and experimental results.

The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.

Object recognition of hyperspectral remote sensing images based on machine learning is widely applied in many industries. However, the efficiency of the training and recognizing process of object recognition on hyperspectral remote sensing images is a critical issue since it involves complex matrix operations and large scale training data sets, especially for resource-constrained devices. One solution is to outsource the heavy workload of object recognition on hyperspectral remote sensing images to a cloud server. Nonetheless, it may bring some security problems when the cloud server is untrustworthy. Therefore, how to enable resource-constrained devices to securely and efficiently accomplish the training and recognizing process of object recognition on hyperspectral remote sensing images is of significant importance. In this article, we propose a secure and efficient scheme to outsource the object recognition on hyperspectral remote sensing images to the untrustworthy cloud server. The proposed scheme can protect the privacy of the computation input and output. Also, we develop an effective verification approach in our scheme that can detect the misbehavior of cloud server with the optimal probability 1. The theoretical analysis and experimental results indicate that our proposed scheme is secure and efficient.

Bilinear pairing is a fundamental operation that is widely used in cryptographic algorithms (e.g., identity-based cryptographic algorithms) to secure IoT applications. Nonetheless, the time complexity of bilinear pairing is$O(n^{3})$, making it a very time-consuming operation, especially for resource-constrained IoT devices. Secure outsourcing of bilinear pairing has been studied in recent years to enable computationally weak devices to securely outsource the bilinear pairing to untrustworthy cloud servers. However, the state-of-art algorithms often require to precompute and store some values, which results in storage burden for devices. In the Internet of Things, devices are generally with very limited storage capacity. Thus, the existing algorithms do not fit the IoT well. In this article, we propose a secure outsourcing algorithm of bilinear pairings, which does not require precomputations. In the proposed algorithm, the outsourcer side’s efficiency is significantly improved compared with executing the original bilinear pairing operation. At the same time, the privacy of the input and output is ensured. Also, we apply the Ethereum blockchain in our outsourcing algorithm to enable fair payments, which ensures that the cloud server gets paid only when he correctly accomplished the outsourced work. The theoretical analysis and experimental results show that the proposed algorithm is efficient and secure.

Rivest-Shamir-Adleman (RSA) is one of the widely deployed public-key algorithms. Yet, its decryption facet is very time consuming for resource-constrained Internet-of-Thing (IoT) devices, as it is based on the modular exponentiation of a large number. Although several variants of RSA have been designed to accelerate decryption, the outcomes have been far from satisfactory. Therefore, it is of imminent importance to investigate how to securely outsource RSA decryption to computational powerful parties as an alternative solution. In this article, we introduce the first efficient and secure outsourcing scheme for RSA decryption in IoT. Though RSA decryption is achieved via modular exponentiation, existing secure outsourcing schemes for modular exponentiation either assume the modulus to be prime and are not applicable to RSA or incur massive computation costs and are heavy laden in practice. To address these issues, we have designed our scheme based on the Chinese remainder theorem (CRT). In our scheme, the private keys (including the exponent and the modulus) and the plaintext are concealed concurrently, and the proposed scheme is highly efficient for both client and cloud. In addition, our scheme enables the client to detect any misbehavior of the cloud server with a probability of 99.17%. To validate the effectiveness of our proposed scheme, we provide rigorous proofs of security and verifiability, as well as efficiency analysis. The effectiveness and efficiency of our scheme are further confirmed based on experimental results.

Solving quadratic congruences is a widely applied operation in cryptographic protocols to ensure the data secrecy in the Internet of Things (IoT). Yet it requires unaffordable computation resource for resource-constrained IoT devices when bulk of this type of operations need to be performed. How to efficiently and effectively solve quadratic congruences on IoT devices becomes a challenging issue. To address this problem, in this article, we propose two practical and secure outsourcing algorithms for solving quadratic congruences. Our proposed algorithms enable the IoT devices to outsource the heavy computation of solving quadratic congruences to a single cloud server, and therefore, achieve high efficiency for IoT devices. Meanwhile, we obscure the input and the output so that the outsourcing process does not leak the privacy of the computation, and the IoT devices in our algorithms can detect any misbehavior of the cloud server with a probability of 1. In addition, we take the Rabin encryption algorithm as an example to show how our proposed algorithms can be applied to IoT applications. The theoretical analysis and experimental results support the fact that our proposed algorithms are secure and efficient.

With the explosive growth of mobile devices and subsequent traffic volume, densified deployment of Heterogeneous Network (HetNet) coexisting with a variety of cells with overlay coverage has emerged as a viable solution for future 5G networks. Despite many advantages, this new architecture also introduces numerous new network management issues, such as frequent handovers. Although a number of handover mechanisms have been proposed, these methods generally depend on one or more objective attributes from the perspective of the users and network, and do not consider the subjective demands of personalized users and specific applications that demand differentiated network services. In this paper, we first develop an software defined networking (SDN)-driven handover architecture that is capable of perceiving global network statements and requirements from all perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN controller to provide differentiated services. Considering the many complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and fuzzy information described in natural language. The evaluation results demonstrate that our scheme outperforms the baseline Received Signal Strength Indicator (RSSI)-based handover scheme, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction.

Unmanned Aerial Vehicles (UAVs), also known as drones, have become a new paradigm to provide emergency wireless communication infrastructure when conventional base stations are damaged or unavailable. In this paper, we propose new schemes to enable the 3D deployment of drones, which can provide network coverage and connectivity services for users located in uneven terrain. We formalize two models, including optimal coverage model and optimal connectivity model, which belong to NP-hard. To be specific, we first consider both the quality of service (QoS) requirements of users and the capacity of drones. We then formalize the problem and design a heuristic scheme, called Particle Swarm Optimization (PSO) algorithm to achieve a cost-effective solution. We also address the optimal connectivity problem in a scenario, in which a number of isolated local networks have been established by users through ad hoc communication and/or device-to-device (D2D) communication. We further develop the cost-effective heuristic algorithm to effectively minimize the total number of required drones. Via extensive performance evaluation, our experimental results demonstrate that the proposed schemes can achieve the effective deployment of drones for users in uneven terrain with respect to the number of required drones.

Transportation-based Cyber-Physical Systems (TCPS), also known as Intelligent Transportation Systems (ITS), have been introduced to increase traffic efficiency and safety. To reduce traffic congestion and traveling time, a number of real-time route guidance schemes have been developed to assist travelers in determining the optimal route for their transit. In this paper, we address the vulnerability issue of the route guiding process and study data integrity attacks against route guidance schemes. To be specific, we consider a generic attack, in which the adversary may compromise vehicles via wireless communication networks and then manipulate the real-time traffic information generated or forwarded by these vehicles, and finally broadcast the forged real-time traffic information into vehicular networks. We formally model the attack and quantitatively analyze its impact on the effectiveness of route guidance schemes. Our findings show that the investigated data integrity attack can effectively disrupt route guidance, resulting in significant traffic congestion, the increase of travel time, and the imbalanced use of transportation resources.

In this paper, we investigate the data integrity attack against Optimal Power Flow (OPF) with the least effort from the adversary's perspective. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector (with a goal to minimize the amount of information to manipulate) as an optimal attack strategy. To defend against such an attack, we develop the defensive scheme by protecting the critical nodes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes.

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive real-world trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher.

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user's experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

In this paper, we address the security issue of optimal power flow (OPF) (as a key component in the smart grid). To be specific, we investigate the data integrity attack against OPF with the least effort from the adversary's perspective, and propose effectively defense schemes to combat the data integrity attack, with respect to the number of nodes to compromise and the amount of information to manipulate. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector as an optimal attack strategy. To defend against such an attack, we develop the defensive schemes by not only protecting the critical nodes but also detecting the existence of attacks based on false measurement detection schemes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes. The experimental results show that the discovered compromised nodes and critical attack vector could lead to the increase of the fuel cost from the power generation by compromising the least number of nodes and injecting the least amount of false information, in comparison with the random attack as the baseline attack strategy. In addition, our two developed defensive schemes are capable of making OPF resilient to the data integrity attack via protecting critical nodes and identifying the falsified measurements accurately in the system.

In recent years, the smart grid has been recognized as an important form of the Internet of Things application. In the smart grid, as an energy-based cyber-physical system, the advanced metering infrastructure (AMI) will be developed to monitor and control the power grid by integrating computing and networking components to ensure stable and efficient operation. The AMI is vulnerable to cyber attacks, especially data integrity attacks. There have been a number of research efforts on detecting such attacks. Nonetheless, most of existing schemes either rely on predefined thresholds or require external knowledge. This may lead to low detection accuracy when the thresholds are improperly defined, and where there is a lack of the external knowledge. To address these issues, in this paper, we propose a Gaussian-mixture model-based detection scheme to mitigate data integrity attacks. Not relying upon the predefined thresholds or external knowledge, our developed scheme operates through narrowing the range of normal data, which can be obtained through clustering the historical data and learning minimum and maximum values or distance values to each center of individual clusters. To evaluate the effectiveness of our proposed scheme, we conduct performance simulation based on the ElectricityLoadDiagrams20112014 data set, and then analyze the effectiveness of the proposed scheme with respect to detection accuracy and overhead. The results of our investigation show that our scheme could achieve a higher detection rate, and a lower error rate, in comparison to existing schemes based on the Min-Max model.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.

In the smart grid, the Advanced Metering Infrastructure (AMI) will be deployed to monitor and control the power grid by integrating both computing and networking components to achieve stable and efficient operation. The AMI is vulnerable to cyber attacks, especially in the form of data integrity attacks. A number of research efforts have been devoted to detecting such attacks. Nonetheless, the majority of existing schemes either rely on a pre-defined threshold, or require external knowledge. This leaves open the possibility for low detection accuracy when the threshold is improperly defined, and where there is a lack of the requisite external knowledge. To address this issue, in this paper we propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to combat data integrity attacks. Not relying upon the pre-defined threshold or external knowledge, our scheme operates by narrowing the range of normal data that can be obtained by clustering the historical data and learning the minimum and maximum values of individual clusters. To validate the effectiveness of our scheme, we conduct performance evaluation based on the ElectricityLoadDiagrams20112014 data set, and analyze the effectiveness of the proposed scheme with respect to detection accuracy.The results of our investigation demonstrate that our scheme can achieve a higher detection rate, and lower error rate, in comparison with existing schemes based on the Min-Max model.

In this paper, we address the issue of designing an effective distributed real-time pricing scheme in the smart grid and investigating its security resilience when the data integrity attack is in place. Different from existing research efforts, in this paper we develop a distributed real-time pricing scheme, which can maximize the welfare of all participants and improve the resilience to system failures, as well as consider both renewable and traditional power resources. By leveraging the distributed approach, we leverage the gradient projection mechanism to solve the distributed real-time pricing problem in participants' smart meters to improve the resilience to system failures. We also investigate the vulnerabilities of the distributed real-time pricing scheme by considering one typical data integrity attack, which can inject false data into communication interfaces. Via a combination of both theoretical analysis and performance evaluation, we demonstrate that the proposed distributed scheme can effectively guide the participants to achieve individual welfare maximization. Our findings also show that data integrity attacks can disrupt the distributed real-time pricing, posing a damage to the welfare of participants.

In the smart grid, as a large-scale distributed cyber-physical system, Demand Response (DR) plays an important role in the electricity market. Various demand response schemes have been developed to improve the efficiency and economy of power utilization. Nonetheless, most existing schemes, including both market-led and system-led schemes, do not carefully take the information security into account in the DR process so that the power grid could suffer from cyber attacks (data integrity attacks, etc.). To address this issue, in this paper we proposed a resilient demand response scheme based on microgrids, which can achieve both great effectiveness of energy use and security resilience against data integrity attacks. In our scheme, the DR process considers two power distribution stages. In the intra-microgrid stage, the DR providers generate the list of possible electricity prices and schedules for power delivery. In the inter-microgrid stage, utilities select the proper electricity price and the schedule for power delivery. In this way, the damage impact of attacks on the power grid can be limited only within isolated microgrids that are compromised, while other microgrids that are not compromised can operate effectively. Our experimental results show that our scheme can not only bring better benefits to all participants, but also achieve a greater security resilience in the DR process in comparison with existing schemes.

The multistep electricity price (MEP) policy has been introduced by many countries to promote energy saving, load balancing, and fairness in electricity consumption. Nonetheless, with the development of the smart grid, how to determine the quantity of electricity and at what price in a step-like fashion has not been fully investigated in the past. To address this issue, in this paper, we introduce two types of MEP models: a one-dimensional MEP model and a two-dimensional MEP model, which can be used to formally analyze and determine the desirable quantities of electricity and pricing in multiple steps. Particularly, in the one-dimensional MEP model, the steps are scaled only by the quantity of electricity whereas in the two-dimensional MEP model, the steps are scaled by both the quantity of electricity and the time when the electricity is used. Based on the proposed MEP models, we further investigate the vulnerability of the electricity market operation and investigate false data injection attacks against electricity prices and charges to consumers. Through an extensive simulation study, our data shows that the proposed MEP models can achieve fairness in electricity consumption, balance loads between peak and non-peak times, and improve electricity resource utilization. Our data also indicates that false data injection attacks can only partially compromise prices in our MEP models, leading to a limited impact on users' charges.

In real-time monitoring systems, fine-grained measurements would pose great privacy threats to the participants as real-time measurements could disclose accurate people-centric activities. Differential privacy has been proposed to formalize and guide the design of privacy-preserving schemes. Nonetheless, due to the correlations and high fluctuations in time-series data, it is hard to achieve an effective privacy and utility tradeoff by differential privacy mechanisms. To address this issue, in this paper, we first proposed novel multi-dimensional decomposition based schemes to compress the noise and enhance the utility in differential privacy. The key idea is to decompose the measurements into multi-dimensional records and to achieve differential privacy in bounded dimensions so that the error caused by unbounded measurements can be significantly reduced. We then extended our developed scheme and developed a binary decomposition scheme for privacy-preserving time-series aggregation in real-time monitoring systems. Through a combination of extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources, and can effectively guide the participants to achieve individual welfare maximization. Particularly, we develop a Lagrangian- based approach that transforms the global optimization conducted by the power company to distributed optimization problems. We show that these distributed problems are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks, i.e., injecting false data into demand-users and injecting false data into supply- users. Through a combination of theoretical analysis and performance evaluation, our data shows that the proposed real-time pricing scheme can effectively guide the participants to achieve welfare maximization. Our data also shows that data integrity attacks can effectively disrupt the results of real-time pricing decisions, posing welfare reduction on participants.

Real-time monitoring systems can introduce numerous benefits to the participants in terms of performing data mining and analysis. Nonetheless, due to the correlations in time-series data, it is hard to achieve an effective privacy and utility tradeoff through a normal differential privacy mechanism. To address this issue, we propose novel multi-dimensional decomposition based schemes, which can greatly improve the utility in differential privacy. After extending the developed scheme, we then develop a binary decomposition scheme for time-series aggregation in real-time monitoring systems. Through both extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.

To enhance the reliability and efficiency of energy service in the smart grid, the concept of the microgrid has been proposed. Nonetheless, how to secure the dynamic microgrid partition process is essential in the smart grid. In this paper, we address the security issue of the dynamic microgrid partition process and systematically investigate three false data injection attacks against the dynamic microgrid partition process. Particularly, we first discussed the dynamic microgrid partition problem based on a Connected Graph Constrained Knapsack Problem (CGKP) algorithm. We then developed a theoretical model and carried out simulations to investigate the impacts of these false data injection attacks on the effectiveness of the dynamic microgrid partition process. Our theoretical and simulation results show that the investigated false data injection attacks can disrupt the dynamic microgrid partition process and pose negative impacts on the balance of energy demand and supply within microgrids such as an increased number of lack-nodes and increased energy loss in microgrids.

In an intelligence transportation system (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining the optimal route for their travels. In order to determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and reduce travel time. Particularly, DEDR considers real-time traffic information generation and transmission. Based on the shared traffic information, DEDR introduces Trust Probability to predict traffic conditions and dynamically en-route determine alternative optimal routes. In addition, DEDR considers multiple metrics to comprehensively assess traffic conditions and drivers can determine optimal route with individual preference of these metrics during travel. DEDR also considers effects of external factors (e.g., Bad weather, incidents, etc.) on traffic conditions. Through a combination of extensive theoretical analysis and simulation experiments, our data shows that DEDR can greatly increase the efficiency of an ITS in terms of great time efficiency and balancing efficiency in comparison with existing schemes.

The smart grid is a new type of energy-based cyber-physical system (CPS), which enables interactions between the utility provider and customers through smart meters and advanced metering infrastructures (AMI). Nonetheless, an adversary can inject misleading energy usage information to the utility provider through compromised smart meters and disrupt the grid and electricity market operations. To address this issue, in this paper, we propose an Energy Dispatching False Data Defense (EDF2D) approach, which can effectively detect the forged interactive information between customers and the utility provider with a great accuracy and mitigate the damage raised by attacks on grid operations. Particularly, EDF2D uses the historical interactive information of normal users to determine the conditional probabilities of data anomalies. Based on these conditional probabilities, a Bayesian network designed for detecting false data can be established by EDF2D, and this network is then used to confirm the authenticity of interactive information received by the utility provider originally transmitted from customers. Through a combination of theoretical analysis and performance evaluation, our experimental data shows that EDF2D can effectively detect harmful false interactive data forged by the adversary and mitigate false data injection attacks on smart grid operations.

In Cyber-Physical Networked Systems (CPNS), the adversary can inject false measurements into the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromise-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. PCREF adopts polynomials instead of Message Authentication Codes (MACs) for endorsing measurement reports to achieve resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial, derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Through extensive theoretical analysis and experiments, our data shows that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

The concept of Multistep Electricity Price (MEP) policy has been introduced by many countries to promote energy saving, load balance and fairness in electricity consumption. However, with the development of smart grid, how to determine the electricity quantity and price scaled to multiple steps has not been fully investigated. To address this issue, in this paper we study a two-dimensional MEP model to formally analyze and determine the desirable electricity quantity and price in multiple steps. In this model, the step is scaled by both electricity quantity and the time when the electricity is used. Based on the proposed MEP model, we further investigate the vulnerability of the electricity market operation and investigate false data injection attacks against electricity price and charges to consumers. Through simulation study, our data show that the proposed MEP models can achieve the fairness in electricity consumption, balance in load between peak time and non-peak time and improvement of resource utilization. Our data also indicates that false data injection attacks can only partially compromise prices in the MEP model, leading to a limited impact on users' charges.

The smart grid shall not only integrate the intermittent resources (IRs) to meet the diverse demands of users and reduce the greenhouse gas emission, but also integrate Electricity Vehicles (EVs) as the energy storage facility to smooth the bulk power generation over time. In this paper, we model and analyze the impact of integrating IRs and EVs on the bulk power generation in the smart grid. In particular, we introduce the reliability ratio to quantify the power generation capacity of intermittent resources and model the process of charging and discharging of EVs as a queuing system. We extend the Security-Constrained Economic Dispatch (SCED) and include the reliability limit of IRs and the number of EVs in the power generation dispatch process and formally analyze the effect of IRs and EVs on the bulk power generation. We conduct extensive simulation and our data shows that increasing IRs can decrease the bulk generation and the curve of bulk generation over time becomes smooth as the number of EVs increases.

The smart grid poses great concern about the exposure of consumers' privacy as the fine-grained measurements in the smart metering system can expose consumer's privacy through the disclosure of accurate load profiles of home energy usage. To address this issue, in this paper we propose novel scaling perturbation based privacy-preserving schemes that can achieve great utility for fine-grained measurements in a privacy-friendly and cost-effective manner. Our schemes adopt the measurement-based scaling perturbation to hide original measurements with low cost. Through a combination of both extensive theoretical analysis and experiments, our results show that the proposed schemes can preserve consumers' privacy through fine-grained measurements and achieve a better utility-privacy tradeoff in comparison with the existing schemes.

In Cyber-Physical Networked Systems (CPNS), attackers could inject false measurements to the controller through compromised sensor nodes, which not only threaten the security of the system, but also consumes network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromised-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. Particularly, PCREF adopts polynomials instead of MACs (message authentication codes) for endorsing measurement reports to achieve the resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Via extensive theoretical analysis and simulation experiments, our data show that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

In wireless sensor networks (WSNs), attackers could inject false data into the networks by compromising the sensor nodes. False data injected by the compromised nodes, if undetected, could not only cause false alarms but also consume the limited energy of the sensor nodes, posing serious threats to the lifetime of networks. To mitigate this type of attacks, a number of en-route filtering schemes to filter false data inside the networks have been developed in the past. However, there is lack of a systematical strategy to evaluate those schemes and establishing a foundation for designing en-route filtering techniques. To address these issues, we compare the pros and cons of the existing enroute filtering schemes. To fairly compare the performance of those schemes, we conduct theoretical analysis and derive a set of closed formulae for them. Our extensive simulations validate our findings. Our research summarizes the state-of-art research development and lay out future directions in this area.