This survey examines the current state of the Metaverse, encompassing its fundamental concepts, technological framework, practical applications, and user experience to evaluate its stage of development. This paper reviews the core concepts of the Metaverse and Extended Reality (XR) and evaluates the latest advancements in hardware and software technologies. Furthermore, it examines the Metaverse’s typical applications in four key domains: education, training, medicine, and mixed life, while summarizing user feedback to identify its advantages and challenges. The feedback indicates that the Metaverse offers notable benefits, including immersive experiences, enhanced training effectiveness, cost efficiency, and improved safety. However, significant challenges remain, such as hardware performance limitations, software inefficiencies, user discomfort, health risks, and social and ethical concerns. The analysis suggests that while the Metaverse has yet to reach full maturity, it holds great potential for future development. To further advance the field, this paper highlights key research priorities in artificial intelligence, quantum computing, and social governance, providing insights for future studies.

Low-Rank Adaptation (LoRA) has become the de facto parameter-efficient fine-tuning (PEFT) method for large language models (LLMs) by constraining weight updates to low-rank matrices. Recent works such as Tied-LoRA, VeRA, and VB-LoRA push efficiency further by introducing additional constraints to reduce the trainable parameter space. In this paper, we show that the parameter space reduction strategies employed by these LoRA variants can be formulated within a unified framework, Uni-LoRA, where the LoRA parameter space, flattened as a high-dimensional vector space R^D, can be reconstructed through a projection from a subspace R^d, with d << D. We demonstrate that the fundamental difference among various LoRA methods lies in the choice of the projection matrix, P ∈ R^{D×d}. Most existing LoRA variants rely on layer-wise or structure-specific projections that limit cross-layer parameter sharing, thereby compromising parameter efficiency. In light of this, we introduce an efficient and theoretically grounded projection matrix that is isometric, enabling global parameter sharing and reducing computation overhead. Furthermore, under the unified view of Uni-LoRA, this design requires only a single trainable vector to reconstruct LoRA parameters for the entire LLM -- making Uni-LoRA both a unified framework and a “one-vector-only” solution. Extensive experiments on GLUE, mathematical reasoning, and instruction tuning benchmarks demonstrate that Uni-LoRA achieves state-of-the-art parameter efficiency while outperforming or matching prior approaches in predictive performance.

Multimodal sentiment analysis plays a critical role in numerous IoT-driven applications, such as personalized smart assistants, healthcare monitoring systems, and intelligent transportation networks, where accurate interpretation of user emotions is vital for enhancing service quality. However, a severe threat of privacy leakage in the multimodal sentiment analysis has been overlooked by previous works. To fill this gap, we propose a Differentially Private Correlated Representation Learning (DPCRL) model to achieve privacy-preserving multimodal sentiment analysis by combining a correlated representation learning scheme with a differential privacy protection scheme. Our correlated representation learning scheme aims to achieve heterogeneous multimodal data transformation to meet the requirements of privacy-preserving multimodal sentiment analysis by learning the correlated and uncorrelated representations, where especially, a pre-determined correlation factor is employed to flexibly adjust the expected correlation among the correlated representations. The differential privacy protection scheme is used to obtain the disturbed correlated and uncorrelated representations by adding Laplace noise for -differential privacy. In particular, the correlation factor can help alleviate the side-effect of the added Laplace noise on the sentiment prediction performance. Finally, via conducting a series of real-data experiments, we validate that our proposed DPCRL model is superior to the state of the art for privacy-preserving multimodal sentiment analysis.

Advancements in the Internet of Medical Things (IoMT) have made remote patient monitoring increasingly viable. However, challenges persist in safeguarding sensitive data, optimizing resources, and addressing the energy constraints of patient devices. This paper presents a health monitoring framework integrating Mobile Edge Computing (MEC) and sixth-generation (6G) technologies, structured into internal Medical Body Area Networks (int-MBANs) and external communications beyond MBANs (ext-MBANs). For int-MBANs, the proposed OptiBand algorithm optimizes energy consumption, extends device standby time, and considers message timeliness and medical criticality. A key innovation of OptiBand is its incorporation of patient’s device standby time into the resource allocation strategy to address real-world patient needs. For ext-MBANs, the DynaMEC algorithm dynamically balances energy efficiency, privacy protection, latency, and fairness, even under varying patient scales. A latency-aware scheduling mechanism also be introduced to guarantee timely completion of emergency tasks. Theoretical analysis and experimental results confirm the feasibility, convergence, and optimality of both algorithms. These characteristics and advantages of the proposed system make remote patient monitoring through IoMT more feasible and effective.

Current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brain-computer interfaces, it is time to explore next-generation recommenders that show users' real-time thoughts without delay. Electroencephalography (EEG) is a promising method of collecting brain signals because of its convenience and mobility. Currently, there is only few research on EEG-based recommendations due to the complexity of learning human brain activity. To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition Theory and Graph Convolutional Networks for accurate item recommendations. Compared with the state-of-the-art recommendation models, the superiority of QUARK is confirmed via extensive experiments.

Machine learning models contain much information about the training dataset, so even if some data points are deleted, the private information can still be inferred. To counteract this problem, "machine unlearning", as an emerging data management approach, is proposed to remove data from the databases and the influence of data from the trained models. Such a technique is vital in the current era of data-driven applications, where the privacy and security of users can be guaranteed. Yet, machine unlearning is still in its early stage, and there are rare existing methods for machine unlearning in the federated setting that is a more practical and crucial scenario. Therefore, this paper investigates the federated machine unlearning problem where the local clients of a federated system intend to delete their local private data appropriately. The proposed method is termed Approximate Federated unlearning (Appro-Fun), which adopts differential privacy and second-order optimization to achieve (ϵ, δ)-approximate unlearning on trained models. Rigorous theoretic analysis presents the performance guarantee of Appro-Fun, and real-data experiments validate the advantages of Appro-Fun compared with the state-of-the-art.

When multimodal AI systems increasingly utilize diverse data sources to achieve advanced understanding and interaction, they inevitably collect vast amounts of sensitive information, thus highlighting the urgent need for robust privacy safeguards, especially as these technologies expand into fields like healthcare, finance, and education. Existing research on data privacy in AI, encompassing adversarial training-based models, differential privacy-based models, and differentially private transform-based models, often neglects the inter-correlation inherent in multi-sensor data. To address this gap, we propose the differentiAl Private OnLine muLti-sensor data predictiOn model (APOLLO), which simultaneously considers intra-correlation and inter-correlation to enhance privacy protection while maintaining predictive performance. Under the proposed APOLLO frame-work, we design two implementations: APOLLO I, which ensures$\epsilon$-differential privacy by adding Laplace noise to each correlated data segment, and APOLLO II, which applies additional noise to make the concatenated multi-sensor data realize$\epsilon{-}$differential privacy. Furthermore, we conduct the theoretical analysis to reveal the relationship between performance influence and the privacy budget, providing guidelines for noise addition with the aim of achieving certified performance. Comprehensive experiments validate the effectiveness of the APOLLO model, establishing a new standard for privacy-preserving multi-sensor data prediction.

Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs, which is crucial in detecting malicious activities, network analysis, social network analysis, and more. Despite its paramount importance, conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data. This paper introduces a neural-based inexact graph de-anonymization, which comprises an embedding phase, a comparison phase, and a matching procedure. The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs. The comparison phase uses a neural tensor network to ascertain node resemblances. The matching procedure employs a refined greedy algorithm to discern optimal node pairings. Additionally, we comprehensively evaluate its performance via well-conducted experiments on various real datasets. The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.

The study of generative models is a promising branch of deep learning techniques, which has been successfully applied to different scenarios, such as Artificial Intelligence and the Internet of Things. While in most of the existing works, the generative models are realized as a centralized structure, raising the threats of security and privacy and the overburden of communication costs. Rare efforts have been committed to investigating distributed generative models, especially when the training data comes from multiple heterogeneous sources under realistic IoT settings. In this paper, to handle this challenging problem, we design a federated generative model framework that can learn a powerful generator for the hierarchical IoT systems. Particularly, our generative model framework can solve the problem of distributed data generation on multi-source heterogeneous data in two scenarios, i.e., feature related scenario and label related scenario. In addition, in our federated generative models, we develop a synchronous and an asynchronous updating methods to satisfy different application requirements. Extensive experiments on a simulated dataset and multiple real datasets are conducted to evaluate the data generation performance of our proposed generative models through comparison with the state-of-the-arts.

Machine unlearning is an emerging need that aims to remove the influence of deleted data from a learned model in a timely manner. Thus, unlearning is important for privacy and security in data management. Nevertheless, existing machine unlearning methods fail to perform exactly and efficiently in a federated setting. In this paper, we study the unlearning problem in federated learning, which provides a data deletion mechanism in the federated setting. First of all, a quantized federated learning (Q-FL) algorithm is developed to facilitate exact unlearning. Based on the quantized federated learning system, an exact and efficient federated unlearning (Exact-Fun) algorithm is designed to realize the goal of data deletion. Through theoretic analysis and experimental evaluation, our proposed methods not only have the desired unlearning effectiveness but also achieve high unlearning efficiency compared with the existing works.

3D grey image segmentation has become a promising approach to facilitate practical applications with the help of advanced deep learning models. Although a number of previous works have investigated the vulnerability of deep learning models to backdoor attack, there is no work to study the severe risk of backdoor attack on 3D grey image segmentation. To this end, we propose two backdoor attack methods on 3D grey image segmentation, including Full-control Backdoor Attack (FCBA) and Partial-control Backdoor Attack (PCBA), on 3D grey image segmentation by leveraging a frequency trigger injection function and a rotation-based label corruption function. Our proposed trigger injection function is applied to insert a 3D trigger pattern into the benign 3D grey images in the frequency domain while ensuring the invisibility of the trigger pattern. And the proposed rotation-based label corruption function is employed to yield the crafted labels with the aim of decreasing the performance of segmentation. Finally, through comprehensive experiments on a real-world dataset, we demonstrate the effectiveness of our proposed backdoor models, the frequency trigger injection function, and the rotation-based label corruption function.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

As one of the most promising machine learning frameworks emerging in recent years, Federated learning (FL) has received lots of attention. The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally. However, recent studies have shown that traditional centralized federated learning is vulnerable to various attacks, such as gradient attacks, where a malicious server collects local model gradients and uses them to recover the private data stored on the client. In this paper, we propose a DEcentralized FEderated learning Against aTtacks (DEFEAT) framework and use it to defend the gradient attack. The decentralized structure adopted by this paper uses a peer-to-peer network to transmit, aggregate, and update local models. In DEFEAT, the participating clients only need to communicate with their single-hop neighbors to learn the global model, in which the model accuracy and communication cost during the training process of DEFEAT are well balanced. Through a series of experiments and detailed case studies on real datasets, we evauate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.

Recent progress has witnessed the excellent success of neural networks in many emerging applications, such as image recognition, text classification, and speech analysis. In order to achieve secure communication, the utilization of neural networks has been realized yet has not raised sufficient research attention. In addition, the existing neural network-based communication system falls short due to its critical security flaws. In this article, we investigate the security vulnerabilities of the existing neural communication system. Based on our analysis, we design two kinds of attack models, includingtarget man-in-the-middle attackandtarget fraud attack. After that, to improve the security performance of neural communication systems, we develop a new defense mechanism to facilitate two-way secure communication by separating secret key from plaintext and incorporating defensive loss into the training process. Moreover, we show the effectiveness of our proposed neural communication system via theoretical proof. Finally, we implement comprehensive real data experiments to evaluate the performance of our attack and defense methods from the aspects of classification accuracy, communication efficiency and communication qualify, which confirms the advantages of our proposed neural communication system compared with the state-of-the-art.

Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.

As a research hotspot for graph mining technology, Graph Convolutional Networks (GCN) have achieved remarkable performance in the fields of wireless networks, Internet of Things, and edge computing. However, recent studies have shown that GCN is vulnerable to adversarial attack; that is, even imperceptible intentional perturbations on graph structure or node attributes can significantly change classification results. This paper proposes a novel graph convolutional network, Pairwise Gaussian Graph Convolutional Networks (PGGCN), in which a pairwise architecture is designed for GCN model construction and training. This elegant design enables PGGCN to mitigate the effects of adversarial attack and thus improve model robustness while guaranteeing classification accuracy. The performance of PGGCN is validated through extensive experimental results, which confirm that PGGCN can effectively improve the robustness of GCN while ensuring classification accuracy.

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks , and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption , and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

The demand of sharing video streaming extremely increases due to the proliferation of Internet of Things (IoT) devices in recent years, and the explosive development of artificial intelligent (AI) detection techniques has made visual privacy protection more urgent and difficult than ever before. Although a number of approaches have been proposed, their essential drawbacks limit the effect of visual privacy protection in real applications. In this article, we propose a cycle vector-quantized variational autoencoder (cycle-VQ-VAE) framework to encode and decode the video with its extracted audio, which takes the advantage of multiple heterogeneous data sources in the video itself to protect individuals’ privacy. In our cycle-VQ-VAE framework, a fusion mechanism is designed to integrate the video and its extracted audio. Particularly, the extracted audio works as the random noise with a nonpatterned distribution, which outperforms the noise that follows a patterned distribution for hiding visual information in the video. Under this framework, we design two models, including the frame-to-frame (F2F) model and video-to-video (V2V) model, to obtain privacy-preserving video streaming. In F2F, the video is processed as a sequence of frames; while, in V2V, the relations between frames are utilized to deal with the video, greatly improving the performance of privacy protection, video compression, and video reconstruction. Moreover, the video streaming is compressed in our encoding process, which can resist side-channel inference attack during video transmission and reduce video transmission time. Through the real-data experiments, we validate the superiority of our models (F2F and V2V) over the existing methods in visual privacy protection, visual quality preservation, and video transmission efficiency. The codes of our model implementation and more experimental results are now available athttps://github.com/ahahnut/cycle-VQ-VAE.

Under the needs of processing huge amounts of data, providing high-quality service, and protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has been treated as a promising technique to facilitate distributed learning with privacy protection. Although the importance of developing privacy-preserving FL has attracted a lot of attentions, the existing research only focuses on FL with independent identically distributed (i.i.d.) data and lacks study of non-i.i.d. scenario. What is worse, the assumption of i.i.d. data is impractical, reducing the performance of privacy protection in real applications. In this article, we carry out an innovative exploration of privacy protection in FL with non-i.i.d. data. First, a thorough analysis on privacy leakage in FL is conducted with proving the performance upper bound of privacy inference attack. Based on our analysis, a novel algorithm, 2DP-FL, is designed to achieve differential privacy by adding noise during training local models and when distributing global model. Especially, our 2DP-FL algorithm has a flexibility of noise addition to meet various needs and has a convergence upper bound. Finally, the real-data experiments can validate the results of our the oretical analysis and the advantages of 2DP-FL in privacy protection, learning convergence, and model accuracy.

Vehicle counting is a fundamental component in Intelligent Transportation System (ITS) for city traffic management. Although a number of vehicle counting approaches have been proposed, their essential drawbacks limit the efficacy of vehicle counting in real applications. In this paper, we propose a CityCam-to-Edge cooperative learning framework by cooperating multiple city cameras with an edge server to count vehicles more efficiently. Our learning framework consists of a lightweight feature extraction scheme deployed on the city cameras and a vehicle counting model implemented on the edge server. We devise the lightweight feature extraction scheme by leveraging multiple convolutional layers with few kernels in the design of deep learning architecture to reduce the utilization of parameters for feature extraction, so that the city cameras’ memory consumption and the data transmission time can be greatly reduced. Moreover, we design two novel vehicle counting models, F2F-M and O2O-M, to improve the counting performance by exploiting the temporal correlation among videos captured from multiple city cameras in a frame-to-frame manner and a video-to-video manner, respectively. By combining the lightweight feature extraction scheme and the proposed vehicle counting models, we obtain two end-to-end vehicle counting models, Lite-F2F-M and Lite-O2O-M. Finally, via conducting extensive experiments, we demonstrate that Lite-F2F-M and Lite-O2O-M models outperform the state-of-the-art in terms of vehicle counting accuracy and time efficiency.

Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.

Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media, etc., has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph-structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, the existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this article, we propose adversarial privacy graph embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph data sets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/KaiyangLi1992/Privacy-Preserving-Social-Network-Embedding.

Computer vision and deep neural networks have been significantly promoting the development of visual perception in these years. Particularly, for autonomous vehicles, real-time image/video data is captured by onboard cameras and analyzed by computer vision techniques in many real applications. In the captured camera data, some contents can be used as auxiliary information to infer individuals' locations and trajectories, which leads to severe privacy leakage but has been rarely studied. Thus, the goal of this article is to protect individuals' location privacy by hiding side-channel information in the captured data while preserving the data utility for downstream applications. To this end, the technology of generative adversarial networks (GAN) is utilized to design two novel models, named ADGAN-I and ADGAN-II, both of which can take the original camera data as inputs and generate privacy-preserving outputs according to predefined sensitive object class. Thus, the processed camera data can defend location inference attack from adversaries in offline applications. Moreover, in ADGAN-I and ADGAN-II, the tradeoff between location privacy and data utility can be effectively balanced. Finally, the results of extensive real-data experiments validate the superiority of our proposed models over the state of the arts in utility preservation and privacy protection for autonomous vehicles' images and videos.

To promote development of Mobile Crowdsensing Systems (MCSs), numerous auction schemes have been proposed to motivate mobile users' participation. But, task diversity of MCSs has not been fully explored by most existing works. To further exploit task diversity and improve performance of MCSs, in this paper, we investigate the joint problem of sensing task assignment and schedule with considering multi-dimensional task diversity, including partial fulfillment, bilaterally-multi-schedule, attribute diversity, and price diversity. First, task owner-centric auction model is formulated and two distributed auction schemes (CPAS and TPAS) are proposed such that each task owner can locally process auction procedure. Then, mobile user-centric auction model is established and two distributed auction schemes (VPAS and DPAS) are developed to facilitate local auction implementation. These four auction schemes differ in their approaches to determine winners and compute payments. We further rigorously prove that all the four auction schemes (CPAS, TPAS, VPAS, and DPAS) are computationally-efficient, individually-rational, and incentive-compatible and that both CPAS and TPAS are budget-feasible. Finally, we comprehensively evaluate the effectiveness of CPAS, TPAS, VPAS, and DPAS via comparing with the state-of-the-art in real-data experiments.

In recent years, a series of researches have revealed that the Deep Neural Network (DNN) is vulnerable to adversarial attack, and a number of attack methods have been proposed. Among those methods, an extremely sly type of attack named the one‐pixel attack can mislead DNNs to misclassify an image via only modifying one pixel of the image, leading to severe security threats to DNN‐based information systems. Currently, no method can really detect the one‐pixel attack, for which the blank will be filled by this paper. This paper proposes two detection methods, including trigger detection and candidate detection. The trigger detection method analyzes the vulnerability of DNN models and gives the most suspected pixel that is modified by the one‐pixel attack. The candidate detection method identifies a set of most suspected pixels using a differential evolution‐based heuristic algorithm. The real‐data experiments show that the trigger detection method has a detection success rate of 9.1%, and the candidate detection method achieves a detection success rate of 30.1%, which can validate the effectiveness of our methods.

Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.

Motivating the mobile users to participate in sensing services for efficient data generation and collection is one of the most critical issues in Mobile Crowdsensing Systems (MCSs). Auction based mechanisms are seen to be promising and effective solutions to incentivize mobile users. However, price is not the unique factor dominating participants' contribution in MCSs. Participant's preference for different sensing tasks is also a pivotal factor which should be considered in the auction mechanisms as assigning the least favorite tasks discourages them to participate in future sensing tasks. Unfortunately, participant's preference has been overlooked by most existing works, which motivates us to fill this gap in this paper. We first propose a new concept "mutual preference degree" to capture participant's preference and then design a preference-based auction mechanism (PreAM) to simultaneously guarantee individual rationality, budget feasibility, preference truthfulness, and price truthfulness. Finally, both the theoretical analysis and simulation results demonstrate the effectiveness of PreAM.

The autonomous driving (auto-driving) technology has been promoted significantly by the rapid advances in computer vision and deep neural networks. Auto-driving vehicles, nowadays, are fully equipped with numerous sensors such as cameras, geo-sensors, and radar sensors, to capture real-time data inside the vehicles and outside surroundings. Meanwhile, the captured data contains lots of private information about vehicles, drivers and passengers and thus faces a high risk of privacy breaches. Especially, side-channel information can be mined from camera data to identify vehicles' locations and even trajectories, raising serious privacy issues. Unfortunately, the issue, how to resist location-inference attack for camera data in auto-driving, has never been addressed in literature. In this paper, we intend to fill this blank by developing a GAN-based image-toimage translation method named Auto-Driving GAN (ADGAN). Through performance comparisons between ADGAN and the state-of-the-art, the superiority of ADGAN can be validated - offering an effective tradeoff between recognition utility and privacy protection for camera data.

IP geolocation determines geographical location by the IP address of Internet hosts. IP geolocation is widely used by target advertising, online fraud detection, cyber-attacks attribution and so on. It has gained much more attentions in these years since more and more physical devices are connected to cyberspace. Most geolocation methods cannot resolve the geolocation accuracy for those devices with few landmarks around. In this paper, we propose a novel geolocation approach that is based on common routers as secondary landmarks (Common Routers-based Geolocation, CRG). We search plenty of common routers by topology discovery among web server landmarks. We use statistical learning to study localized (delay, hop)-distance correlation and locate these common routers. We locate the accurate positions of common routers and convert them as secondary landmarks to help improve the feasibility of our geolocation system in areas that landmarks are sparsely distributed. We manage to improve the geolocation accuracy and decrease the maximum geolocation error compared to one of the state-of-the-art geolocation methods. At the end of this paper, we discuss the reason of the efficiency of our method and our future research.

In the age of emerging applications, such as Internet of Things (IoT), big data, and data mining, our life becomes more convenient through customized services that utilize a huge amount of personal data generated and collected by various IoT devices. To fully exploit the data value as well as enhance the data utilization, more and more data are being traded in online data markets. While enjoying the benefit from data trading, data sellers are also suffering from severe risk of privacy leakage. In this paper, our objective is to maximize data seller's received utility via balancing the tradeoff between data trading benefit and data privacy cost. To achieve this, contract theory is utilized to design optimal contract trading mechanisms for both complete and incomplete information markets. From our thorough theoretical analysis, comprehensive simulations, and real-data experiments, the effectiveness of our proposed optimal contract mechanisms can be validated, i.e., the maximum utility can be obtained at the seller side, the individual rationality and incentive compatibility can be guaranteed at the buyer side, and the advantages of our mechanism over the single contract mechanisms can be confirmed.

With dramatic developments of blockchain technology, a number of blockchain-based applications emerge rapidly, among which the incorporation of blockchain into Internet of Things is one of the most valued research direction. Such powerful incorporation is a double-sided sword, i.e., it can benefit both individuals and society but has the vulnerability to coin hopping attack that is a new type of pool mining attack and hard to happen in traditional blockchain networks. In this paper, we theoretically prove the feasibility of coin hopping attack, deeply analyze the conditions of attack implementation, and comprehensively investigate the impacts of coin hopping attack. Moreover, some defense strategies are addressed. To our best knowledge, this paper is the first work targeting coin hopping attack.

Smart cyber-physical systems (CPSs) include Internet of things (IoT), smart grids, smart cities, smart transportation, and smart "Anything" (e.g., homes and hospitals).ese systems require different levels of security and protection based the sensitivity of their data.Nonetheless, we are living in a world where cyber attacks, privacy violations, phishing scams, and data breaches have become commonplace.Smart CPSs are also subject to security violations and privacy breaches, which stem from the vulnerabilities of existing computers and communications technologies.In addition, as smart CPSs get more complex, more vulnerabilities will emerge.Hackers will be able to launch increasingly sophisticated attacks in the future due to the ever-shi ing cyber physical landscape.Hence, innovative research is needed for security assurance and privacy preservation in smart CPSs for new architectural models, system designs, and cryptographical protocols.In this special issue, we received submissions from both academia and industry in the relevant fields.Following a strict review process, we accepted papers for this special issue.Each of the papers was peer-reviewed by at least three experts in the field.In the following, we provide a brief introduction to each paper.ere are four papers aiming to design and analyze security schemes and privacy preserving strategies for IoT applications.e paper titled "Function-Aware Anomaly Detection Based on Wavelet Neural Network for Industrial Control Communication" proposed a function-aware anomaly detection approach to detect these cyber intrusions and anomalies.Next, the authors of the paper titled "A Compatible OpenFlow Platform for Enabling Security Enhancement in

Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the collusion attack.

With the emergence of Mobile Crowdsensing Systems (MCSs), many auction schemes have been proposed to incentivize mobile users to participate in sensing activities. However, in most of the existing work, the heterogeneity of MCSs has not been fully exploited. To tackle this issue, in this paper, we study the joint problem of sensing task assignment and scheduling while considering partial fulfillment, attribute diversity, and price diversity. We first elaborately model the problem as a reverse auction and design a distributed auction framework. Then, based on this framework, we propose two distributed auction schemes, cost-preferred auction scheme (CPAS) and time schedule-preferred auction scheme (TPAS), which differ on the methods of task scheduling, winner determination, and payment computation. We further rigorously prove that both CPAS and TPAS can achieve computational-efficiency, individual-rationality, budget-balance, and truthfulness. Finally, the simulation results validate the effectiveness of both CPAS and TPAS in terms of sensing task's allocation efficiency, mobile user's working time utilization and utility, and truthfulness.

Spanner construction is one of the most important techniques for topology control in wireless networks. A spanner can help not only to decrease the number of links and to maintain connectivity but also to ensure that the distance between any pair of communication nodes is within some constant factor from the shortest possible distance. Due to the non-locality, constructing a spanner is especially challenging under the physical interference model signal-to-interference-and-noise-ratio (SINR). In this paper, we develop two localized randomized algorithms SINR-directed-YG and SINR-undirected-YG to construct a directed Yao graph (YG) and an undirected YG in O(log n) (n is the number of wireless nodes) time slots with a high probability, in which each node is capable of performing successful local broadcasts to gather neighborhood information within a certain region and the SINR constraint is satisfied at all the steps of the algorithms. The resultant graph of SINR-undirected-YG, which is based on SINR-directed-YG, possesses a constant stretch factor 1/1-2 sin(π/c), where c > 6 is a constant. To the best of our knowledge, SINR-undirected-YG is the first spanner construction algorithm under SINR. We also obtain Yao-Yao graph under SINR. Extensive theoretical performance analysis and simulation study are carried out to verify the effectiveness and the efficiency of our proposed algorithms.

Due to the popularity of auction mechanisms in real-world applications and the increasing awareness of securing private information, auctions are in dire need of bid-privacy protection. In this paper, we design three secure, multi-unit, sealed-bid, first-price auction schemes. The first is a secure auction using homomorphic encryption and is denoted by SAHE; the second is a secure action using masking values and is denoted by SAMV; and the third has an improved masked noise algorithm, denoted by ISAMV. In the first, SAHE, the auction is processed on encrypted bids by a server, and the final output is only known by the auctioneer. Neither the auctioneer nor the server can obtain the full information of the bidders. The second and third auctions, SAMV and ISAMV, decrease computational complexity. Instead of homomorphic encryption, they use random noise to mask the bid values. By using a masking method, the server only knows the noise, and the auctioneer only knows the auction results; neither will see the private information of the bidders. All three schemes enable the auctioneer to verify that the winners have paid the correct amounts. A thorough theoretical analysis is performed to evaluate the security properties, computational complexity, and communication complexity of the auctions. Copyright © 2016 John Wiley & Sons, Ltd.

As a distributed system, a wireless network, usually faces a complex environment (transient faults and topology changes occur frequently). The connected dominating set (CDS) problem has been widely studied due to its important applications in wireless communication and networks, especially the important role as a virtual backbone for efficient routing. In this paper, under SINR (Signal-to-Interference-plus-Noise-Ratio) model, we propose a distributed self-stabilizing maximal independent set (MIS) algorithm (DSSMIS). Based on DSSMIS, we design a distributed self-stabilizing algorithm (DSSCDS) for CDS construction with constant approximation within O(log n) rounds. To best of our knowledge, this is the first self-stabilizing CDS algorithm under SINR model.

In this paper, we propose an extensible and flexible truthful auction framework that is individual-rational and self-collusion resistant. By properly setting one simple parameter, this framework can yield efficient auctions (like VCG) and (sub)optimal auctions (like Myerson's Optimal Mechanism (MOM)) with a more computationally-efficient procedure compared to VCG and MOM; by carefully choosing virtual valuation functions for the bidders, it can produce attribute-aware auctions that take the channel diversity into consideration. The framework adopts a novel procedure that can prevent bidder self-collusion resulted from the bid diversity. Theoretical analysis and case studies demonstrate the strength of our auction framework in handling various considerations in a practical heterogeneous spectrum market.

In this paper, we investigate the problem of achieving proportional fairness via access point (AP) association in multirate WLANs. This problem is formulated as a nonlinear programming with an objective function of maximizing the total user bandwidth utilities in the whole network. Such a formulation jointly considers fairness and AP selection. We first propose a centralized algorithm Non-Linear Approximation Optimization for Proportional Fairness (NLAO-PF) to derive the user-AP association via relaxation. Since the relaxation may cause a large integrality gap, a compensation function is introduced to ensure that our algorithm can achieve at least half of the optimal in the worst case. This algorithm is assumed to be adopted periodically for resource management. To handle the case of dynamic user membership, we propose a distributed heuristic Best Performance First (BPF) based on a novel performance revenue function, which provides an AP selection criterion for newcomers. When an existing user leaves the network, the transmission times of other users associated with the same AP can be redistributed easily based on NLAO-PF. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms to those of the state of the art.

As one of the most powerful tools in game theory, double auction is widely utilized to tackle the spectrum allocation problem in a secondary market. In this paper, we propose a multi-unit double auction framework in which the conflict graph-based bidder group formation, the winner determination strategy, and the spectrum pricing are elaborately designed. Through an in-depth theoretical analysis, we prove that our auction scheme can achieve three critical properties including the individual rationality, the ex-post budget balance, and the truthfulness. Extensive simulation results validate that the proposed auction framework can significantly improve the user satisfaction degree.

The cooperation between the primary and the secondary users has attracted a lot of attention in cognitive radio networks. However, most existing research mainly focuses on the single-hop relay selection for a primary transmitter-receiver pair, which might not be able to fully explore the benefit brought by cooperative transmissions. In this paper, we study the problem of multi-hop relay selection by applying the network formation game. In order to mitigate interference and reduce delay, we propose a cooperation framework FTCO by considering the spectrum sharing in both the time and the frequency domain. Then we formulate the multi-hop relay selection problem as a network formation game, in which the multi-hop relay path is computed via performing the primary player's strategies in the form of link operations. We also devise a distributed dynamic algorithm PRADA to obtain a global-path stable network. Finally, we conduct extensive numerical experiments and our results indicate that cooperative multi-hop relaying can significantly benefit both the primary and the secondary network, and that the network graph resulted from our PRADA algorithm can achieve the global-path stability.

This paper investigates the problem of spectrum assignment and sharing to minimize the total delay of multiple concurrent flows in multi-hop cognitive radio networks. We first analyze the expected per-hop delay, which incorporates the sensing delay and transmission delay characterizing the PU activities and spectrum capacities. Then we formulate a minimum delay optimization problem with interference constraints, and propose an approximation algorithm termed MCC to solve the problem. According to our theoretical analysis, MCC has a bounded performance ratio and a low computational complexity. Finally, we exploit the minimum potential delay fairness in spectrum sharing to mitigate the inter-flow contentions. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms with that of the state-of-the-art.

In this study, we investigate the problem of partially overlapping channel assignment to improve the performance of 802.11 wireless networks. We first derive a novel interference model that takes into account both the adjacent channel separation and the physical distance of the two nodes employing adjacent channels. This model defines “node orthogonality”, which states that two nodes over adjacent channels are orthogonal if they are physically sufficiently separated. We propose an approximate algorithm MICA to minimize the total interference for throughput maximization. Extensive simulation study has been performed to validate our design and to compare the performances of our algorithm with those of the state-of-the-art.

Mobile IPv6 (MIPv6) offers a basic solution to support mobility in IPv6 networks. Although Hierarchical MIPv6 (HMIPv6) has been designed to enhance the performance of MIPv6 by hierarchizing the network, it does not always outperform MIPv6. In fact, two solutions have different application scopes. Existing work studies the impact of various parameters on the performance of MIPv6 and HMIPv6, but without analyzing their application scopes. In this paper, we propose a model to analyze the application scopes of MIPv6 and HMIPv6, through which an Optimal Choice of Mobility Management (OCMM) scheme is designed. Different from the existing work that either propose new mobility management schemes or enhance existing mobility management schemes, OCMM chooses the better alternative between MIPv6 and HMIPv6 according to the mobility and service characteristics of users, addressing whether to hierarchize the network. Besides that, OCMM chooses the best mobility anchor point and regional size when HMIPv6 is adopted, addressing how to hierarchize the network. Simulation results demonstrate the impact of key parameters on the application scopes of MIPv6 and HMIPv6 as well as the optimal regional size of HMIPv6. Finally, we show that OCMM outperforms MIPv6 and HMIPv6 in terms of total cost including average registration and packet delivery costs.

In this paper, we consider how to achieve proportional fairness in multi-rate 802.11 WLANs by investigating an integrated problem of power control and AP Association in order to provide an effective tradeoff between network throughput and fairness. Since jointly considering power control and AP association for proportional fairness is NP-hard, we propose a centralized heuristic approach. By introducing a new concept of AP utility, we establish the relationship between the network utility and the AP utility according to proportional fairness. This relationship is exploited to design an algorithm PCAP to optimize the network utility by increasing the average and decreasing the variance of the AP utility. Extensive simulation study is performed and the results demonstrate that PCAP yields a significant improvement in terms of throughput, fairness, and power consumption compared to other popular power control algorithms.