Driven by recent advances of large models and agents, embodied artificial intelligence (AI) emerges as a transformative paradigm for next-generation AI, endowing agents with physical forms and the ability to perceive, reason, and act within real-world environments. Unlike disembodied or virtual AI agent systems, embodied agents co-evolve cognition, control, and embodiment through continuous feedback loops, enabling applications ranging from humanoid robots to autonomous vehicles. In this survey, we first introduce a dual-brain architecture of embodied AI and examine its foundational technologies and key characteristics. We then analyze the security and privacy landscape, identifying critical vulnerabilities and evaluating existing/potential countermeasures. Finally, we outline emerging trends and open research directions in this emerging field, charting a roadmap toward efficient, secure, and ethically aligned embodied AI ecosystems.

Differential-privacy federated learning (DP-FL) has emerged as a promising paradigm capable of mitigating the inherent threat of traditional FL architectures that are vulnerable to inferential attacks due to the frequent exchange and updating of model parameters. However, existing DP-FL frameworks often assume that the client's perturbations remain constant throughout the FL process, while ignoring the varying influence of the client's perturbations in distinct communication rounds on the model performance. Besides, existing DP-FL frameworks posit the FL server as a fully rational actor, thereby neglecting the bounded rationality that the FL server may exhibit in the face of risk and uncertainty. In this paper, we propose a novel long-term (i.e., throughout the FL process) privacy-preserving FL framework to address the optimal incentive design, in the presence of the bounded rationality inherent in the FL server and the dynamic influence of perturbations on model performance. Specifically, we first investigate the impact of local perturbations of the client on the model's convergence performance in different communication rounds, elucidating the trade-off between learning performance and privacy loss. Then, to reconcile learning performance with privacy loss, we design a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, by applying prospect theory (PT) to formulate the risk-aware behavior of the bounded rationality FL server, we employ contract theory to derive the equilibrium of the game, thereby ensuring optimality and fairness. Finally, extensive simulations illustrate that our scheme can motivate clients to provide high-quality models and improve the accuracy of the global model, compared with benchmarks.

Federated learning (FL) is a widely studied framework for privacy-preserving collaborative training among multiple clients. However, real-world deployments reveal a persistent challenge: free-riders, i.e., participants who benefit from the system without contributing meaningful updates. If not properly addressed, free-riders can discourage honest contributors and ultimately impair both the fairness and efficiency of FL ecosystems. Existing defenses mainly rely on post-hoc per-client, per-round evaluation, leading to limited deterrence and high resource overhead, particularly in large-scale deployments. To tackle these problems, we propose FLET, a novel federated learning framework with test tasks. FLET introduces dedicated test tasks into the training process, blending them with real FL tasks while concealing their types from participants. These test tasks, generated from the reference datasets, serve as decoys that enable accurate detection of free-riding behavior. To enforce accountability, an economic penalty mechanism is employed, achieving both proactive (ex-ante) deterrence and reactive (expost) detection.We analyze the interactions between the server and participants through a free-riding suppression game model with asymmetric information (i.e.,task type), and develop a strategic information disclosure scheme (i.e.,revealing task requirements) to mislead attackers and proactively shape participant behavior. We characterize both pure-strategy and mixed-strategy perfect Bayesian Nash equilibria, and propose a lightweight strateg-ymaking algorithm that guides players toward equilibrium strategies under different conditions with modest overhead. Extensive experiments validate that FLET effectively suppresses free-riding and enhances the utility of both the server and participants. Our findings provide insights for designing cost-effective free-riding defenses in practical FL.

Due to the immutable nature of smart contracts, online contract diagnosis is the only viable approach for revealing vulnerabilities in deployed contracts. Existing online approaches face significant challenges in terms of efficiency, adaptability, and reliance on vulnerability labels. This paper proposes ConWatcher+, a new adaptive and label-efficient online contract diagnosis framework from the diffusion perspective, which is capable to detect yet unknown attacks under evolving tactics without reliance on vulnerability labels. ConWatcher+ simulates the Advanced Persistent Threat (APT) tactics commonly used in yet unknown attacks by continuously applying minor perturbations to legitimate interaction behaviors. It then reversely learns the denoising process, guided by potential logic vulnerabilities (i.e., functionality dependencies), to adaptively identify stealthy anomalies and detect yet unknown attacks without needing vulnerability labels. ConWatcher+ proceeds in five steps. First,real-time data extraction. We design a cost-effective contract runtime information collector, incorporating on-demand data retrieval and event-driven data update mechanisms to reduce communication overhead in online contract diagnosis. Second,interaction behavior modeling. Via bytecode-level, account-level, revenue-level modeling, and side-channel level behavior modeling, we propose behavior-aware multivariate time series model to accurately represent long-term contract interactions with multi-faceted behaviors. Third,APT-like noise adding. We leverage the forward diffusion model to produce minor and stochastic APT-like noises with efficiency. Fourth,reverse denoising learning. To effectively guide reverse denoising using functionality dependencies, we devise an adaptive contract-level analysis engine equipped with heterogeneous control flow graph modeling and heterogeneous message passing mechanisms to extract function-level and bytecode-level functionality dependencies. Last,contract anomaly detection. We establish a label-efficient attack detector based on reconstruction error for contract anomaly detection. It combines complex dependency analysis and deterministic inference to ensure high-quality data reconstruction and low detection latency. Extensive empirical validations on a manually constructed dataset, covering both mainstream and novel vulnerabilities, demonstrate ConWatcher+’s effectiveness, adaptability, and label efficiency, with an average F1-score of 0.92 across all types of attacks without prior knowledge of corresponding vulnerabilities.

Fine-tuning large language models (LLMs) is critical for adapting pretrained models to specialized downstream tasks. Federated LLM fine-tuning enables privacy-aware model updates by allowing data owners (DOs) to contribute a global LLM without exposing local data. However, full-parameter fine-tuning in federated settings incurs significant computational and communication overhead, while frequent gradient exchanges increase the risk of privacy leakage, such as memorized data inference. Parameter-efficient fine-tuning (PEFT) with differential privacy (DP) offers a low-overhead alternative with formal privacy guarantees, but fails to strike privacy-utility tradeoff under heterogeneous privacy preferences: individual DOs may inject excessive DP noise to maximize privacy, whereas the curator aims to minimize noise to preserve model quality. In this paper, we present an innovative game-theoretical framework that enables dynamic privacy trading within differentially private federated LLM fine-tuning. In the game, DOs strategically adjust their local DP noise levels in exchange for customized incentives from the curator, thereby balancing privacy and utility. We begin by establishing a theoretical convergence bound that quantifies the influence of locally injected noise on the global model utility. Under this bound, we analytically characterize the pure-strategy Nash equilibrium of the game, accounting for DO heterogeneity, curator budget constraints, and noise estimation errors. For mixed-strategy settings with incomplete information, we design a hierarchical reinforcement learning algorithm that jointly learns DOs’ optimal noise-saving strategies and the curator’s optimal pricing policy without presupposing their private information. Experiments on real-world datasets demonstrate that the proposed scheme improves DO utility, reduces curator cost, mitigates free-riding, and accelerates convergence compared to existing methods.

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

Retrieval-based augmentation enhances the capabilities of large language models (LLMs) by incorporating external knowledge into the response generation process. However, existing retrieval-augmented frameworks often lack fine-grained access control and risk exposing sensitive content, particularly in voice-based interactive systems where queries are open-ended and personalized. This risk becomes especially pronounced when the retrieved information includes proprietary or user-specific data. To mitigate these challenges, we propose PrivRAG, a privacy-preserving retrieval protocol that integrates access control and response-level privacy protection throughout the generation pipeline. Specifically, each document in the knowledge base is assigned an attribute-based access policy represented as a logical tree, ensuring that only authorized users can retrieve relevant content. The interactions between user and LLM-driven assistant is modeled as a multi-turn process, where user attributes are inferred through probabilistic reasoning over observed responses. Based on these inferred attributes, the system selectively accesses permitted knowledge segments and generates responses accordingly. To further protect sensitive content, the response is transformed using a formal privacy-preserving mechanism that combines calibrated noise injection for numerical fields with semantic generalization for textual entities. Empirical evaluations on synthetic interactions demonstrate that PrivRAG effectively enforces access control while preserving user privacy, with minimal degradation in response quality across voice-based use cases.

As the Internet of Vehicles (IoV) advances, the security concerns surrounding vehicular networks have grown increasingly critical due to the openness of networking among vehicles, inadvertently creating more opportunities for adversaries to infiltrate and potentially disrupt vehicle operations. Intrusion detection systems (IDSs) stand as a promising solution, effectively mitigating the myriad of threats and security concerns that plague vehicles. In this article, we delve into the realm of IDSs within vehicular networks and propose an innovative collaborative intrusion detection framework based on blockchain technology and auction game. First, we integrate a vehicular blockchain into the IDS, offering a holistic approach to tackling both internal and external threats within vehicular networks. Second, we introduce a novel assistant-delegated Byzantine fault tolerance (A-DBFT) consensus algorithm, designed to bolster the efficiency of intrusion detection within the blockchain while maintaining the efficacy of the consensus mechanism. Third, we develop an auction game mechanism that incentivizes assistants and verifiers to actively initiate and participate in auctions, thereby enhancing the overall security of our intrusion detection scheme. Ultimately, we present simulation results that validate the superiority of our proposed scheme compared to conventional approaches.

Federated learning (FL) model marketplaces require qualified workers to collaboratively train customized models. However, recruiting optimal workers on a limited budget in non-independent and identically distributed (non-IID) data settings remains a fundamental issue. Moreover, inadequate quality verification exposes the marketplace to spoofing and poisoning attacks, while verifying data and model quality without accessing local storage remains a significant dilemma. To bridge the research gap, this paper proposes a knowledge-aware model customization scheme in FL model marketplaces, to facilitate zero-trust worker recruitment and verification while ensuring privacy preservation. Specifically, (i) we design a knowledge-aware quality evaluation mechanism by leveraging the knowledge of workers, i.e., soft-label predictions of their local models on a privacy-free reference dataset (provided by the customer), to assess their data quality in a privacy-preserving manner. (ii) We formulate the optimal worker recruitment problem under budget constraints as an NP-hard integer programming problem and design a dynamic programming-based optimal worker recruitment algorithm with budget feasibility and computational efficiency. (iii) We devise a two-stage zero-trust quality verification mechanism by utilizing zero-knowledge proof (ZKP) to exclude distrustful workers, thereby preventing spoofing and poisoning attacks. Extensive experimental results demonstrate that the proposed scheme enhances model customization performance by up to 34.3% on label-skewed non-IID data and 36.2% on feature-skewed non-IID data compared with existing representatives.

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

The convergence of aUtonomous aerial vehicles (UAVs) and federated learning (FL) has emerged as a promising paradigm to facilitate artificial intelligence (AI) services with enhanced privacy preservation. However, notwithstanding the inherent advantages of FL in terms of privacy protection, attackers can still exploit inference attacks to deduce raw data of UAVs. The existing studies predominantly assume FL servers (hereafter servers)to be fully rational and have access to all privacy preference information of UAVs (i.e., information symmetry scenario), in the design of privacy-preserving incentive schemes. To tackle these challenges, we propose a privacy-preserving incentive scheme for UAV-aided FL in the presence of information asymmetry while considering the serverexhibits bounded rationality. Specifically, a practical UAV-aided FL framework is first introduced to enable AI model training between UAVs and the server with bounded rationality. In addition, based on differential privacy, we quantify the privacy level of UAVs and subsequently analyze its impact on the aggregation accuracy of the server. This scenario entails two conflicting objectives: the server aims for higher-quality local models to achieve better aggregation accuracy, while UAVs prioritize injecting more noise into their local models to enhance privacy protection. To reconcile the conflicting objectives, we develop an incentive mechanism based on contract theory to optimize the server’s aggregation accuracy in the presence of information asymmetry. Furthermore, we employ prospect theory (PT) to the above contract to capture biases in the server’s subjective decision-making process. Besides, we deduce closed-form solutions for optimal contracts under PT and expected utility theory (EUT), where participants are assumed to be fully rational. Finally, simulation results validate the superiority of our proposed scheme in motivating UAVs to share high-quality local models and improving the aggregation accuracy of the server.

The integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) has garnered significant interest as a promising paradigm for facilitating intelligent and pervasive mobile crowdsensing (MCS) services. In traditional AI methodologies, the centralization of large volumes of privacy-sensitive sensory data shared by UAVs for model training entails substantial privacy risks. Federated learning (FL) emerges as an appealing privacy-preserving paradigm that enables participating UAVs to collaboratively train shared models while safeguarding the privacy of their data. However, given that the execution of FL tasks inherently requires the consumption of resources such as power and bandwidth, rational and self-interested UAVs may not actively engage in FL or launch free-riding attacks (i.e., sharing fake local models) to mitigate costs. To address the above challenges, we propose a truthful incentive scheme in FL-based UAV-assisted MCS. Specifically, we first present a learning framework tailored for realistic scenarios in UAV-assisted MCS that enhances privacy preservation and optimizes communication efficiency during AI model training for collaborative UAVs, where the sensing platform (i.e., the aggregation server) is the finite-rational decision maker. Then, based on prospect theory (PT), we design an incentive mechanism to motivate UAVs to participate in FL. In this mechanism, a PT-based game is exploited to model the interactions between the sensing platform and UAVs, where the equilibrium is derived. Moreover, we employ a zero-payment mechanism to curb the self-interested behavior of UAVs. Finally, simulation results show that the proposed scheme can facilitate high-quality model sharing while suppressing free-riding attacks.

Differential private learning is widely used in machine learning (ML) to protect continuous and scalar-valued data. The demand for discrete and matrix-valued computations is increasing, particularly in quantized neural networks and graph learning, which require discrete-valued parameters and large-scale matrix operations for efficient data processing. However, privacy protection for discrete and matrix-valued data is less explored. Traditional differential private mechanisms fail to maintain the discrete nature of data after perturbation and often overlook data correlations, struggling to balance privacy and utility. In this paper, we propose a Discrete Matrixing Differentially Private Learning (DM-DPL) framework, which protects the privacy of discrete and matrix-valued data during ML training by adding discrete matrix-variate Gaussian noise. First, we propose a novel Discrete Matrix-Variate Gaussian (DMVG) mechanism with rigorous conditions necessary to guarantee (ϵ, δ)-differential privacy. Additionally, we present an eigenvalue-weighted analysis-based precision budget allocation strategy, designed to maintain the utility of significant dimensions while providing consistent privacy guarantees. Finally, the results illustrate that our approach significantly surpasses existing state-of-the-art methods when applied to quantized federated learning. To the best of our knowledge, this is the first work to specifically protect discrete and matrix-valued data during ML training.

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats.

The rise of the Metaverse has ushered in a new era of social networking, offering users deeply engaging spaces to connect and participate in social activities. However, rendering these virtual environments is resource-intensive. With many users accessing simultaneously and requiring diverse Metaverse services, optimizing Metaverse resources to deliver the best quality-of-experience (QoE) for users is a significant challenge. In this paper, we propose a cooperative virtual reality (VR) rendering and dynamic resource leasing mechanism to address this issue. Specifically, we first introduce a cooperative VR scene pre-rendering framework between users and Planets (i.e., edge servers hosting users), and establish a new user QoE metric named EdgeVRQoE which considers both rendering delay and visual quality. We formulate the multidimensional rendering resources (e.g., GPU, CPU, and outbound bandwidth) leasing problem between Planets and users as a double-layer decision problem, and devise a hybrid action multi-agent reinforcement learning-based dynamic resource auction mechanism to efficiently allocate limited resources of Planets in a distributed and adaptive manner. Extensive simulations demonstrate that our proposed scheme outperforms the representatives in user QoE and resource utilization efficiency. Particularly, the proposed scheme shows at least an 18-fold improvement in QoE over other schemes, demonstrating its capability in providing immersive Metaverse experiences.

Aerial intelligent reflecting surface (AIRS), integrating unmanned aerial vehicle (UAV) with IRS, has emerged as a promising paradigm to improve the transmission quality and security in emergency communication, space-air-ground-integrated network and mobile edge computing, etc. However, the size of a single AIRS is constrained by the limited energy and payload capacity of the UAV, as well as the path loss of the air-to-ground reflective link, which makes the gain from a single AIRS finite. To address these problems, we propose a hybrid aerial IRS-assisted cooperative secure transmission system, where an aerial active IRS and an aerial simultaneously transmitting and reflecting IRS (STAR-IRS) are employed to achieve reflection amplification and 360-degree ubiquitous coverage, respectively. Additionally, the cooperative beamforming gain generated by the secondary reflection between the hybrid AIRSs can further improve communication quality. Specifically, an optimization problem is proposed with the objective of maximizing the sum secrecy rate by jointly optimizing the transmitting beamforming and the reflection coefficients of each AIRS. We first reformulated the original non-convex problem by fractional programming method, and a three-layer alternating optimization algorithm is introduced to address the proposed problem with the successive convex approximation (SCA) as well as penalty convex-concave procedure (PCCP) techniques. Finally, extensive simulations are conducted to demonstrate that the proposed scheme substantially improves the sum secrecy rate compared to other baseline schemes.

This paper introduces defensive adversarial attacks aimed at enhancing the security of semantic communication systems by confusing potential eavesdroppers. Existing research predominantly focuses on enhancing the accuracy of semantic communications while neglecting the security vulnerabilities posed by eavesdroppers. In this study, from the standpoint of physical layer security, defensive adversarial attacks are employed to introduce artificial noise into semantic communications, effectively concealing real information. This artificial noise is generated by deep neural networks to mislead eavesdroppers into perceiving the content of images as unrelated information, with little probability of disrupting normal semantic communications. Experimental results demonstrate that the proposed model can selectively mislead the decoding efforts of eavesdroppers, while ensuring uninterrupted decoding by legitimate receivers.

Provenance graph-based auditing offers a promising direction for APT (Advanced Persistent Threat) detection with traceability guarantees. However, most of the existing methods are based on host-level causality analysis, which is ineffective in practical APT scenarios when well-organized adversaries exploit lateral movement attacks (e.g., multi-level proxies) across multiple compromised hosts. To bridge the research gap, this paper proposes a collaborative APT detection and tracing frame-work (TRACEGADGET) based on federal provenance graphs. TRACEGADGET can efficiently reveal the whole trace of APT lateral movements through the interactions between hosts in Intranet. Specifically, the proposed framework 1) characterizes the relevance weights of all events in the given provenance graph in comparison to the POI (Point of Interest) events, 2) identifies the network entries rankings of the POI events through backward trace analysis, 3) reveals the evolution of the alarm events and confirms the network exit of penetration chain through forward propagation, and 4) aligns the network entries and network exits to derive the complete path of the lateral movement attack. Finally, we construct a dataset consisting of 280,000 edges and more than 90,000 entities through ten sets of real APT attacks. We demonstrate the feasibility and effectiveness of the proposed framework in recovering APT attack links at the network level. Particularly, TRACEGADGET achieves 100% APT path reconstruction with high robustness in all the experiments.

Unmanned Underwater Vehicles (UUVs) have been increasingly used in underwater scenes, such as ocean exploration, marine rescue, and underwater pipeline. Underwater wireless optical communication (UWOC) technology has higher speed, higher bandwidth, and lower latency compared to traditional underwater communication technologies, making it a promising paradigm for the communication between UUVs. However, UWOC-enabled UUV networks face challenges in terms of the number of affordable network interfaces and the stringent energy limit due to the expensive cost of network interface for UUVs and the serious underwater working environment. In this paper, we propose an exact algorithm and an approximate algorithm to optimize the network topology to lower energy consumption for UUVs. The exact algorithm can effectively enumerate all the network topology to find the optimal one, which works for small-scale UUV networks with no more than 10 UUVs. The approximate algorithm uses local search algorithm to approach the exact answer in the time complexity of$O(kn^{2}\log n)$, which targets at large-scale UUV networks with 10 ~ 100 UUVs.$k$is the number of iterations, and$n$is the number of UUVs. Simulation results verify the accuracy and efficiency of the proposed two algorithms, and demonstrate when$k=2n^{2}$or$k=3n^{2}$, the relative error of the approximate algorithm is less than 0.5% when$n<10$and converges fast when$n=100$.

Crowd computing in large-scale cellular networks typically involves a significant number of participants, leading to high spectrum interference, reduced communication efficiency, and low user trustworthiness. To overcome these challenges, this paper proposes trustworthy and spectrum-efficient crowd computing scheme in massive multiple-input multiple-output (MIMO) networks based on deep neural networks (DNNs). Existing machine learning-aided multiple antenna technologies usually ignore the pilot contamination, which reduces spectrum efficiency. Here, we leverage the DNN to devise detection and precoding algorithms by inputting an imperfect channel state information (CSI) big data and provides detection and precoding matrices as outputs, where the online-to-offline learning framework offloads the training task to servers to reduce the overhead of base station (BS). With the well-trained DNNs, the BS can generate the detection and precoding matrices with low computation overheads. Especially, minimum-mean-square-error (MMSE) triggers between received signals and sources considering channel estimation error are seen as labels to improve spectrum efficiency. Besides, a multi-factor trust model is designed to enhance user authentication security. The simulations and numerical analysis show the proposed scheme can provide higher spectral efficiency, compared to conventional methods.

Federated learning (FL) has gained widespread adoption in Internet of Things (IoT) applications, promoting the evolution of IoT toward Artificial Intelligence of Things (AIoT). However, IoT devices are still vulnerable to various privacy inference attacks in FL. While current solutions aim to protect the privacy of devices during model training, the published model is still at risk from external privacy attacks during model deployment. To address the privacy concerns throughout the entire FL lifecycle, this article proposes a privacy-enhanced and efficient federated knowledge transfer framework for IoT, named PEFKT, which integrates the knowledge transfer method and local differential privacy (LDP) mechanism. In PEFKT, we devise a data diversity-driven grouping strategy to tackle the non-independent and identically distributed (non-IID) issue in IoT. Additionally, we design a quality-aware soft-label aggregation algorithm to facilitate effective knowledge transfer, thereby improving the performance of the student model. Finally, we provide rigorous privacy analysis and validate the feasibility and effectiveness of PEFKT through extensive experiments on real data sets.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provision anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous research has primarily focused on honeypot system design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks remains unexplored. This paper proposes a novel game-theoretical collaborative defense approach to address optimal, fair, and feasible incentive design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs and the network operator under both partial and complete information asymmetry scenarios. The optimal VDD-reward contract design problem with partial information asymmetry is then solved using a contract-theoretic approach that ensures budget feasibility, truthfulness, fairness, and computational efficiency. In addition, under complete information asymmetry, we devise a distributed reinforcement learning algorithm to dynamically design optimal contracts for distinct types of UAVs in the time-varying UAV network. Extensive simulations demonstrate that the proposed scheme can motivate UAV’s cooperation in VDD sharing and improve defensive effectiveness, compared with conventional schemes.

The rapid development of the Internet of Vehicles (IoV) has spurred innovations in Intelligent Transportation Systems (ITS), but it also faces increasingly sophisticated cybersecurity threats. Traditional defense mechanisms often fall short in handling emerging and complex attacks due to the lack of flexibility to adapt to the rapidly evolving IoV environment. An emerging solution is to employ Large Language Models (LLMs), such as ChatGPT, to enhance IoV security, which depends on the quality, quantity, and freshness of the threat data used for fine-tuning. In this paper, we introduce a collaborative vehicular threat sharing framework that utilizes vehicular honeypots to gather threat data for fine-tuning LLMs, thereby bolstering IoV security. Local differential privacy is leveraged to safeguard the vehicles’ privacy. Given that vehicles have different privacy preferences that may change over time, it is critical to design an appropriate incentive mechanism to encourage sustainable participation in the dynamic IoV environment. Moreover, since privacy preferences are the private information of the vehicles, an information asymmetry exists between the vehicles and the IDS cloud server. To address this challenge, we propose a dynamic contract-based incentive mechanism that considers the dynamically changing privacy preference during long-term participation. The optimal contract is derived to maximize the expected utility of the IDS cloud server. Extensive simulation results demonstrate the feasibility of our proposed dynamic contract based incentive mechanism and validate the effectiveness of the LLM-based threat classification in handling complex threats.

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and promote trustworthy data dissemination in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions. To defend against free-riding attacks, we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game to identify malicious AVs in disseminating fake information. Furthermore, for lack of sufficient knowledge on parameters of the network model and the user cost model in dynamic game scenarios, a reinforcement learning based algorithm with hotbooting is developed to obtain the optimal strategies of subscriber AVs and publisher AVs with free-rider prevention. Extensive simulations are conducted, and the results validate that our SPAD can effectively prevent free-riders and enhance the dependability of disseminated contents, compared with conventional schemes.

Unmanned aerial vehicles (UAVs) combined with tagging applications (Apps) have recently attracted considerable attention to enable efficient mobile crowdsensing (MCS) applications in scenarios where an insufficient number of UAVs may be available to perform the sensing tasks. However, there remain potential security and incentive threats for App- and UAV-assisted crowdsensing owing to the presence of malicious UAVs and the selfishness of UAVs. To address these issues, we propose a two-stage secure incentive mechanism in the App- and UAV-assisted MCS. Specifically, we first develop an App- and UAV-assisted MCS framework, where the App tags the location of the sensing task as a point-of-interest (PoI) to attract registered UAVs, thus assisting the platform to complete the sensing task efficiently. To motivate the App to cooperate with the sensing platform, we design a double auction-based incentive mechanism for PoI-tagging tasks in the first stage, where the optimal price for PoI-tagging services is obtained by applying a double auction game. Furthermore, we evaluate each UAV through comprehensive consideration of the performance and security of UAVs for most task-suitable UAV recruitment and malicious UAVs prevention. Additionally, in the second stage, based on the Stackelberg game theory, an incentive mechanism for sensing tasks is proposed to encourage UAV participation. Finally, simulation results and security analysis validate that the proposed mechanism can greatly increase the utility of UAVs and the App while ensuring the security of the sensing process.

A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. As such, SCFL considerably enhances model utility without sacrificing privacy in a low-cost and highly feasible manner. We unfold the design of SCFL in three steps. i) Stable social cluster formation. Considering users’ heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants’ model updates depending on social trust degrees. iii) Distributed convergence. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.

Application (App)-assisted mobile edge crowd- sensing is a promising paradigm, in which Apps are in charge of tagging the location of the sensing tasks as point-of-interest (PoI) to assist the platform in recruiting users to participate in the sensing tasks. However, there exist potential security, incentive, and privacy threats for App-assisted mobile edge crowdsensing (AMECS) due to the presence of malicious Apps, the low-quality shared sensing data, and the vulnerability of wireless communication. Therefore, we propose a differential privacy-based incentive (DPI) scheme for AMECS to provide secure and efficient crowdsensing services while protecting users’ privacy. Specifically, we first propose an App quality management mechanism to correlate the behavior of each App with its quality and then select reliable Apps based on quality thresholds to assist the platform in recruiting users. With the designed mechanism, we further present an auction game-based incentive mechanism to encourage Apps to mark the location of the sensing tasks as PoI. To protect the privacy of users, a privacy-preserving sensing data sharing algorithm is devised based on differential privacy. Further, given the difficulty of obtaining accurate network parameters in practice, a reinforcement learning-based incentive mechanism is designed to encourage users to participate in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme can effectively improve the utilities of users, ensure the security of the crowdsensing process, and protect the privacy of users.

Cross-silo federated learning, as a distributed learning paradigm, allows clients to collaboratively train an artificial intelligence (AI) model and jointly share the model ownership without local data transfer or exposure. However, the valuable AI models are facing fatal intellectual property (IP) infringement threats when offering AI services. Existing researches on IP protection mainly focus on the centralized models (i.e., single ownership), but leave federated models (i.e., shared ownership) unexplored. In this paper, we propose IPSF, a novel shared IP protection framework with all-round verification for multiple owners under cross-silo federated learning. Specifically, instead of embedding private watermarks individually, we adopt joint watermarks and soft labels as a conjoint fingerprint, and present a watermark generative adversarial network (WM-GAN) mechanism to fuse private watermarks and facilitate the integrated verification. We also design a diversity-and similarity-oriented assessment mechanism to support mutual evaluation between private and joint watermarks. Through the designed assessment mechanism, the correlation and variability between private and joint watermarks are dynamically maintained to ensure the stability of WM-GAN and the fairness among users in verification. Extensive experiments validates that our IPSF achieves desirable fidelity and high robustness under attacks.

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.

The emergence of the Metaverse has brought forth a new era of social networks, offering immersive virtual spaces for users to engage in social activities. However, the resource-intensive nature of rendering avatars and virtual scenes places considerable strain on end devices. To improve the Quality of Experience (QoE) for users, the utilization of edge servers’ resources becomes crucial. Moreover, accommodating the diverse QoE requirements and time dynamics of users (e.g., user join/departure, and social activities) escalates the complexity of resource allocation. In this paper, we propose an auction-based dynamic resource allocation algorithm to efficiently and economically allocate various limited resources (e.g., CPU, GPU, RAM, and VRAM) of edge servers to social user groups in a rapid and decentralized manner. First, with heterogeneous and dynamic varying resources at each Planet (i.e., edge server to host Metaverse users), we design an optimal Planet access scheme to help social user groups to determine which Planet to connect. Second, considering the dynamic nature of social applications, e.g., users dynamically join and depart the network with dynamic requirements on resources, we present a multi-round auction game between social user groups and edge servers to compete for the dynamic multi-dimensional resources before each scheduled time period. By using the above mechanisms, our scheme optimizes the dynamic resource utilization by considering the social feature of Metaverse. Using extensive simulations, we demonstrate that the proposed algorithm dynamically and effectively allocates resources for social Metaverse activities, outperforming conventional allocation approaches.

This article investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical-layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile-edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization (ECM) or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process cannot be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this article, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes and ensures that the node paying higher Gas gets stronger computational resources.

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin (DT) is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of DTs (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws, and privacy invasions of IoDT hinder its wide deployment. Besides, the intrinsic characteristics of IoDT, such as decentralized structure, information-centric routing, and semantic communications, entail critical challenges for security service provisioning in IoDT. To this end, this article presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber–physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term cyberattack that has brought severe financial losses and critical infrastructure damages. Existing works mainly focus on APT defense under stable network topologies, while the problem under time-varying dynamic networks (e.g., vehicular networks) remains unexplored, which motivates our work. Besides, the spatiotemporal dynamics in defense resources, complex attackers’ lateral movement behaviors, and lack of timely defense make APT defense a challenging issue under time-varying networks. In this paper, we propose a novel game-theoretical APT defense approach to promote real-time and optimal defense strategy-making under both periodic time-varying and general time-varying environments. Specifically, we first model the interactions between attackers and defenders in an APT process as a dynamic APT repair game, and then formulate the APT damage minimization problem as the precise prevention and control (PPAC) problem. To derive the optimal defense strategy under both latency and defense resource constraints, we further devise an online optimal control-based mechanism integrated with two backtracking-forward algorithms to fastly derive the near-optimal solution of the PPAC problem in real time. Extensive experiments are carried out, and the results demonstrate that our proposed scheme can efficiently obtain optimal defense strategy in 54481 ms under seven attack-defense interactions with 9.64% resource occupancy in stimulated periodic time-varying and general time-varying networks. Besides, even under static networks, our proposed scheme still outperforms existing representative APT defense approaches in terms of service stability and defense resource utilization.

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

The metaverse, as an instance of cyber–physical–social systems (CPSS) that originates in cyber–physical systems (CPS), features growing complexity, and diversity in terms of functionalities, as well as the exponentially increasing demand in network bandwidth and computational resources, thereby leading to exaggerated security threats. However, compared with the extensive attention received by the metaverse, solutions defending against the threats have not kept pace. A major obstacle to such solutions is virtuality–reality-synthesized threats. Therefore, it is imperative to design new paradigms to defend the metaverse effectively. In this article, we advance a parallel system, dubbed ParaDefender, to defend the metaverse against emerging new threats effectively. Inspired by parallel intelligence, ParaDefender comprises artificial cyberspace, computational experiments, and parallel execution. The basic idea is to make artificial and real cyberspaces executed in parallel to mutually guide each other for enhanced security, wherein the parallel execution is scenario driven in the sense that the scenarios originate from all possible spatial–temporal combinations of security threats in the metaverse. We also demonstrate how to land ParaDefender onto real-world applications, including the Industrial Internet of Things (IIoT) security operation application in the industrial metaverse, and the social governance application.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

The emerging federated learning (FL) offers a feasible solution for the privacy preservation of users' sensitive data in training artificial intelligence (AI) models. Meanwhile, differential privacy (DP) is widely used in FL to ensure that data privacy is not disclosed during model training. However, in the practical deployment of DP in FL, a prominent challenge is that most existing FL solutions set the same privacy level for different users, resulting in over-protection for some users while insufficient protection for others. In this paper, we propose a novel federated learning framework with user-level personalized privacy protection (named FLUP) to meet the personalized privacy requirements of different users while maintaining high data utility. In this framework, we propose a user-level personalized DP mechanism that combines a personalized sampling algorithm and Gaussian perturbation to meet each user's personalized differential privacy corresponding to their privacy parameters. Then, we qualitatively analyze the impact of the sampling threshold on model performance. Furthermore, to balance user privacy requirements and AI model performance, we design a utility-aware game model to distributively determine the optimized sampling threshold and the users' differential privacy parameters. Finally, by conducting validation experiments, we demonstrate the feasibility and effectiveness of our proposed framework in terms of model performance as well as user privacy preservation.

The security of distributed communications in UAV rescue networks is promising to be provisioned by blockchain technology. However, due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blocks, which can result in severe security issues (such as Forged deposit address and Double spend attack). These issues has been neglected in literature. This paper proposes a UAVs assisted and incentive based blockchain offline transaction scheme to address the above issues when UAVs and ground users are offline. Particularly, we consider vehicle-to-vehicle (V2V) charging transactions in disaster areas. First, we built an offline channel between charging and discharging electric vehicles (EVs), and then, we design an accountable assertions based UAVs aided penalty algorithm to prevent various attacks. Then, considering selfishness of users, we formulate an incentive model based on Stackelberg game to encourage EVs to participate to the offline V2V charging transactions. Our simulation results demonstrate that our proposed scheme obtain the optimal utilities for EVs, which outperforms the conventional schemes.

Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for marine monitoring and disaster rescue. In AI-enabled UAV applications, UAVs generate amounts of computation-intensive tasks (e.g., image recognition, video processing, and path planning, etc.) that cannot be locally executed by UAVs in time. How to offload the computation-intensive tasks of UAVs timely and effectively has become an urgent challenge. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose a collaborative computation offloading scheme with UAVs and USV fleets in maritime communication networks. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading, and USV fleets are the assistants. Then, to minimize the overall execution time of computation tasks, UAVs determine the optimal ratio of compu-tation tasks offloaded to USV fleets in the worst case. Afterwards, the first sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Simulation results demonstrate that the proposed scheme reduces the overall execution time and improves the expected revenue of the USV fleet as compared to conventional schemes.

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.

In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this article, we develop alightweightvehicularblockchain-enabledsecure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. First, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Second, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace misbehaviors and record data transactions for UAVs and vehicles with improved efficiency and security in reaching consensus. Third, since UAVs and vehicles have little explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, owing to their flexible mobilities and fast deployment. However, UAVs typically have very limited battery and computational capacities, which makes them harder to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue of the battery and computation resource limitation with a fog computing based UAV system. Specifically, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To avoid the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

Autonomous vehicles (AVs) are coming with great potentials to bring safer, greener, and more convenient transportation systems. As AVs rely on radar, camera, and other advanced sensors to sense its surroundings, a salient challenge of AVs is the intrinsic limitations of onboard sensors (e.g., limited awareness range, blind spots, and failure in foggy days). To tackle this problem, we propose a collaborative data sharing scheme for AVs to make up for sensor deficiencies by promoting sensory information sharing in autonomous driving. However, this brings another fundamental issue on how to ensure trust in shared sensory data from distrustful collaborators and how to motivate AVs to participate in data sharing. This work studies this issue by modeling it as a quality-aware optimal sensing task scheduling problem. Specifically, we design an edge computing-enabled architecture where AVs can form collaborative sensing groups in executing sensing tasks. After that, a quality-aware auction-based incentive mechanism is developed to promote AVs’ participation and high-quality data sharing. We also design a reputation model to recruit trustworthy AVs to perform sensing tasks based on their behaviors and social identities. Due to the NP-hardness of problem, we devise a heuristic algorithm to determine the optimal winners and payments in auction with truthfulness and individual rationality guarantees. Lastly, extensive simulations validate that our approach can effectively improve sensing data quality and user utility, compared with conventional schemes.

Unmanned aerial vehicles (UAVs) have brought numerous potentials to establish flexible and reliable emergency networks in disaster areas when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrustful environment and open-access UAV networking. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable to execute heavy security provisioning operations when carrying out complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-aided disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs' honest behaviors. Furthermore, we introduce a novel vehicular fog computing based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs' heavy data processing and storage tasks. To optimally stimulate vehicles to share their idle computing resources, we also design a two-layer reinforcement learning based incentive algorithm for UAVs and ground vehicles in the highly dynamic networks. Simulation results show that RescueChain can effectively accelerate consensus process, enhance user payoffs, and reduce delivery latency, compared with representative existing approaches.

In this article, we propose a physical layer security (PLS) scheme to secure vehicle-to-vehicle (V2V) communications, where vehicles share spectrum resources with cellular users via underlaying device-to-device (D2D) technologies. Existing PLS-assisted V2V communication schemes neglect the issue of the unknown eavesdropper's instantaneous channel state information (CSI), and the management of spectrum resource reuse-incurred interference should be enhanced. Here, we design an artificial noise (AN)-assisted beamforming scheme to protect the V2V communications that does not require the eavesdropper's instantaneous CSI. Especially, the spectrum resource reuse-incurred interference can be eliminated by detection methods. Also, we deduce the expression of secrecy outage probability as the security metric, and minimize the secrecy outage probability via power allocation. The simulations demonstrate that the proposed scheme can reduce the secrecy outage probability compared to conventional methods.

The exponential growth of data generated from increasing smart meters and smart appliances brings about huge potentials for more efficient energy production, pricing, and personalized energy services in smart grids. However, it also causes severe concerns due to improper use of individuals' private data, as well as the lack of transparency and auditability for data usage. To bridge this gap, in this article, we propose a secure and auditable private data sharing (SPDS) scheme under data processing-as-a-service mode in smart grid. Specifically, we first present a novel blockchain-based framework for trust-free private data computation and data usage tracking, where smart contracts are employed to specify fine-grained data usage policies (i.e., who can access what kinds of data, for what purposes, at what price) while the distributed ledgers keep an immutable and transparent record of data usage. A trusted execution environment based off-chain smart contract execution mechanism is exploited as well to process confidential user datasets and relieve the computation overhead in blockchain systems. A two-phase atomic delivery protocol is designed to ensure the atomicity of data transactions in computing result release and payment. Furthermore, based on contract theory, the optimal contracts are designed under information asymmetry to stimulate user's participation and high-quality data sharing while optimizing the payoff of the energy service provider. Extensive simulation results demonstrate that the proposed SPDS can effectively improve the payoffs of participants, compared with conventional schemes.

To accommodate the surge of data traffic in unmanned aerial vehicle (UAV) applications, software defined air-ground integrated networks (SD-AGNs) hold great potentials for efficient resource allocation and intelligent security countermeasures for UAVs. In SD-AGNs, virtualized bandwidth, computing and security resources owned by terrestrial mobile edge computing (MEC) nodes can be dynamically allocated to satisfy UAVs' diverse demands in data transmission and security protection. However, with complicated cooperative interactions among MEC nodes and competition among UAVs, it is of great challenge to allocate both the security and wireless resource in SD-AGNs. In this paper, we propose a security-aware resource sharing scheme for UAVs to jointly allocate bandwidth and security resource in SD-AGNs, using a game-theoretic approach. Specifically, we first investigate a software-defined collaborative mechanism to promote resource utilization for MEC nodes through coalition formation and resource sharing within each coalition. Then, a coalitional game model is presented to construct the Nash-stable coalition structure for MEC nodes. Furthermore, by modeling the interactions among UAVs as a non-cooperative game, their optimal demands of wireless and security resource, as well as the Nash equilibrium, are analyzed in the competitive environment. Simulation results show that the proposed scheme can effectively improve resource efficiency and reduce average delay.

The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.

Vehicular energy network (VEN), as an important part of the Internet of Things for the smart city, can facilitate the renewable energy (RE) transportation over a large geographical area by means of electric vehicles (EVs) through wireless power transfer technology. However, due to the potential security vulnerability in VEN, EV users can be attacked by external or internal adversaries. In addition, owing to the selfishness of EVs, it is a great challenge to optimally schedule the charging/discharging behaviors of EVs to realize regional energy balance in VEN. To tackle the above issues, this paper proposes a blockchain-based secure incentive scheme for energy delivery in VEN. First, a novel permissioned energy blockchain system is introduced in VEN to implement secure energy delivery services for EVs and energy nodes through the use of distributed ledgers and cryptocurrency. Second, a proof of reputation consensus protocol is proposed to efficiently reach consensus in energy blockchain, where the reputation derivation is constructed based on the local trust computing and credibility computing. Third, motivated by the pricing mechanism, an incentive model is developed to stimulate EVs to cooperatively deliver RE to various areas with different electricity loads while maximizing EVs' utilities. Finally, extensive numerical results are provided, which demonstrate the efficiency of the proposed scheme through the comparison with conventional schemes.

In a smart community (SC) with renewable energy sources (RES), flexible charging service can be provisioned to electric vehicles (EVs), where EVs can choose clean energy, traditional energy, or the mixture of them on demand. Considering the existence of various entities in the SC and the limited generation capacity of RES, it becomes of significance yet very challenging to optimally schedule the charging service for EVs with different consumption preferences. In this paper, we propose a charging scheme for EVs in a SC integrated with RES by using a game theoretical approach. Firstly, a three-party energy network is proposed to model the interactions among the power grid, EVs, and aggregators in the smart grid. Secondly, the trust model is presented to improve safety of power trading by evaluating the reliability of aggregators. Thirdly, based on the four-stage stackelberg game, the optimal strategies of three energy entities are analyzed by solving the stackelberg equilibrium (SE). Furthermore, a weighted max-min fairness (WMMF) based algorithm is proposed to fairly allocate the limited renewable power for EVs. Finally, extensive simulations are carried out to evaluate and demonstrate the effectiveness of the proposed scheme through comparison with conventional schemes.