Blockchain-based Federated Learning (BFL) facilitates collaborative model training with guaranteed transparency and accountability across distributed participants. However, BFL encounters a critical security vulnerability: advanced free-riders can exploit the public on-chain model histories to synthesize fabricated gradients without performing actual local training, which is indistinguishable from legitimate contributions. Previous efforts to defense free-rider attack mostly focus on anomaly detection, but they struggle to detect some advanced free-riders. To address this fundamental challenge, we propose BFLGR (Blockchain-based Federated Learning with Gresham's Reversal), which is a novel framework integrating reverse auction mechanisms and a dynamic reputation system. BFLGR employs a dynamic selection algorithm that can eliminate advanced free-riders by exploiting their profit-seeking motives and parasitic dependency on honest participants' contributions. This framework ensures genuine contributions prevail over deceptive submissions, and achieves a paradigmatic shift in BFL security, transforming the transparency-exploitation vulnerability into a strategic advantage for detecting and eliminating malicious participants. Theoretical proofs and extensive experiments show that BFLGR performs well in advanced free-rider attack situation and outperforms our baselines.

Owing to strong capabilities in capturing interactions among objects and concepts, graph data has been treated as an important type of information collected by smart devices in Industrial Internet of Things (IoT), and the distributed training of graph learning models over these devices brings fundamental supports for intelligent services and operations. However, different IoT devices may collect Non-IID graph data due to different roles in the system, and suffer poor performance when only one unified instance of model is trained. Besides, IoT devices usually belong to different communities in Industrial IoT, such that each community pursues both optimized and rational performance when joining in the training process. Considering both challenges, this article proposes a novel meta-computing framework for federated graph learning in Industrial IoT. A collaborative resource allocation task is formulated where devices belonging to different communities adopt limited resources to participate in the training of multiple instances either within or across communities. Two algorithms are introduced for adaptive and rational resource allocation based on whether devices are owned by single or multiple communities. Both algorithms provide guaranteed performance on efficiency and effectiveness, and the fairness among IoT devices are proved. Finally, extensive numerical results have demonstrated the performance of the proposed framework in handling collaborative graph model learning within Industrial IoT.

Distributed mobile devices collect unlabeled graph data from environment. Introducing popular graph contrastive learning (GCL) methods can learn node representations better. However, training high-performance GCL requires large-scale data and graph data collected by the single device is insufficient. Meanwhile, transmitting local data for centralized training suffers from non-negotiate privacy leakage and bandwidth consumption. Federated learning (FL), as a distributed learning paradigm, is commonly used for such issues. Nevertheless, direct combination of FL and GCL struggles to supplement global graph information. This absence results in neighbor information missing, thus causing the local GCL to learn biased node representations. Moreover, the combination also triggers potential gradient explosion owing to the lack of unified learning criteria. In this paper, we propose a federal GCL framework that complements missing structural information and provides unified learning criteria. The key idea is to achieve cross-client node alignment on server through local graph structural importance to reason about the global graph information. We design a hierarchical structural importance scoring method to comprehensively evaluate structural importance, thus server performs effective cross-client aggregation while maintaining local graph privacy. We demonstrate the security and prove the bandwidth-reducing advantage of the proposed framework. Extensive experiments on 3 datasets show the superior performance of our method.

Mining high-quality logical rules is crucial as they can provide beneficial interpretability for predictions. Recent methods that incorporate logical rules into learning tasks have been proven to yield high-quality logical rules successfully. However, existing methods either rely on the rule instances observed to support rule mining, or simply embed the rule head and the rule body to learn from them. Additionally, they can not fully utilize the rich semantic information contained in logical rules and overlook the intrinsic correlations between all relations within the domain. In this paper, we propose a model called Correlation and order-Aware Rule Learning (CARL) that captures deeper semantic information in rules by allowing relations to be co-aware of each other and paying attention to logical sequence sensitivity. CARL utilizes semantic consistency between the rule body and rule head as its learning objective, continuously introducing more semantic information and logically simplifying the rule body while considering logical sequence sensitivity. We explored the internal correlations between domain relations and used the thought of knowledge distillation to simplify modules so that relations in CARL can share or perceive each other’s information or state efficiently. Experiments on link prediction tasks have demonstrated that CARL can learn higher-quality rules and yield state-of-the-art results on four popular public datasets. https://github.com/burning5112/CARL

The Internet of Thing systems have contributed a considerable scale of graphs via numerous devices, like the network topology. These graph data are used in wireless communication and mobile computing for a variety of tasks such as traffic prediction and vehicle communication. Therefore, the analysis of these graph data has shown great significance towards the management of IoTs. Compared to other distributed computing scenarios, IoTs require a wider range and more frequent transmission of data. Considering the constrained communication resources and concerns about data privacy, IoT devices tend to keep the graph data locally. Therefore, it is meaningful to adopt the idea of Federal Graph Neural Network for IoTs, where the fancy Graph Neural Network can be implemented in a distributed manner over graphs in IoTs. This paper proposes a sampling-based framework towards the purpose. It assumes the graphs are vertically partitioned over devices, which means all devices hold an identical set of vertices and edges, and each owns a subset of features. It fits the fact that different devices own heterogeneous sensing capabilities. Then a sampling-based method is proposed for multi-round training of Federal Graph Neural Network, and only partial devices join in the training in each round. The sampling strategy applies the local accuracy of validation of each device to adjust the sampling probabilities accordingly, so as to accelerate the convergence of the global model. Finally, extensive analysis and numerical evaluation verify the advancement of the framework.

The easy and pervasive involvement of devices in Industrial Internet of Things has greatly benefited the implementation and adoption of various smart services. One prominent prerequisite of such trends is the extensive and continuous support and sharing of data and resources among devices. However, previous efforts usually treat the data sharing as one-time task among devices, which are incapable when the data are applied for the distributed and iterative training task of machine learning models. Therefore, this article proposes a novel framework for continuous data sharing in Industrial Internet of Things. The system consists of different system owners, each brings devices and participate the distributed training of models. Specifically, system owners hold different scales of devices, data, and resources, while devices own heterogeneous availability in different time periods. In this case, the goal is to properly assign devices for qualified model training process in different rounds, such that no devices will devote unlimited resources and the overall efforts and consumptions among different owners are balanced. Accordingly, three algorithms for device allocation are proposed, based on whether the availability of devices in each training round are known at the beginning of the training procedure. The analysis shows that all algorithms can achieve a rational allocation for devices and balance the performance among system owners. Finally, evaluation results reveal that the proposed solutions outperform baseline methods in providing better data sharing plans.

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic extension to the boundary of commercialized data statistic analysis, owing to the pervasive availability of low-cost wireless network access and off-the-shelf mobile devices. In such cases, many data consumers post their queries for urban statistic analysis in the system, like the scales of traffics, and then data contributors in IoT networks upload their contents, which can be evaluated by data brokers and responded to data consumers. However, huge volumes of devices bring large scales of data, constituting heavy burdens for data exchange. Even worse, contents in IoT systems are also sensitive as they are usually linked to private physical status of data contributors. The previous studies for IoT data trading fail to provide comprehensive estimation and pricing towards these difficulties. Therefore, this paper proposes a novel framework for the range counting trading over IoT networks by jointly considering data utility, bandwidth consumption, and privacy preservation. The range counting accumulates the number of data items falling in a concerned range of value, providing important information on the underlying data distribution. This paper first proposes a novel sampling-based method with histogram sketching for range counting estimation. The estimator is proved to be unbiased and achieves advanced performance on variance. Then the framework adopts a perturbation mechanism that can further preserve the results under differential privacy. The theoretical analysis shows that the mechanism can guarantee the privacy preservation under a given size of samples and the accuracy requirement of results. Finally, two types of pricing strategies for range counting trading are introduced for different circumstances, providing holistic consideration on how the parameters given in the estimator should be used for data trading. The framework is evaluated by estimating the air pollution levels and the traffic levels with different ranges on the 2014 CityPulse Smart City datasets. The evaluation results demonstrate that our framework can provide more accurate and reliable statistical information, with reduced bandwidth consumption and strengthened privacy preservation.

Internet of Thing (IoT) systems have been treated as a novel platform for graph data acquisition. Contents like dynamic network topology, organization and control flows, and interactions among monitored objects all contribute to the huge volumes of graph data generated in IoT. These data are believed to brought significant benefits to both the operation and functionalities of IoT systems, especially when combined with cutting-edge Artificial Intelligence techniques. However, these graph data are usually locally collected by data contributors with sensing devices, which could be both partially overlapped as they record same environment, and sensitive as they can indicate private physical status of contributors. Considering all challenges, current solutions for graph data collection in IoT are incapable. Therefore, this article proposes a novel framework for privacy-preserving distributed graph data collection for IoT. The framework allows the graphs kept by data contributors to be partially overlapped, and can help the data broker to efficiently derive the universal view by combining these graphs. The differential privacy is applied for privacy preservation during data collection. The proposed problem aims at minimizing the total bandwidth consumption for graph collection, which is proved to be NP-complete. Then three algorithms are proposed for different circumstances, based on the diverse knowledge and purposes held by the data broker. Finally, both theoretical and numerical analysis have demonstrated the advancement of these methods.

Internet of Thing (IoT) systems provide novel opportunities for data acquisition, where sensing devices can flexibly collect and trade data with data brokers. A data broker may conduct sophisticated analysis on the collected data and further exchange statistics like histograms with data requestors. Considering the supply-demand correlations, several pivotal factors must be jointly treated including communication bandwidths, data utilities, privacy issues, total budget, etc. Unfortunately, the current efforts mainly apply the crowdsensing strategies during data trading and overlook the subsequent processing and analysis of the collected data. Therefore, this paper proposes a novel framework for efficient data trading in IoT systems throughout the data collection and data processing phases. In the framework, data contributors can flexibly arrive and departure from the monitored area in heterogeneous time slots. The incentives for data trading are correlated with data volume, channel condition, and privacy issues of each contributor. Meanwhile, a data broker samples partial sensing data and aggregates approximate histograms for data requestors. The objective is to minimize the total budget for data trading. First, the theoretical bound on the necessary budget for histograms with a given accuracy is proved. Then two algorithms are proposed for efficient data trading among data contributors, based on whether the behaviors of data contributors are known in advance. Both algorithms are analyzed and the corresponding guarantees on performance are discussed. Finally, the extensive evaluation results validate the advancement of the proposed algorithms.

The data publication from multiple contributors has been long considered a fundamental task for data processing in various domains. It has been treated as one prominent prerequisite for enabling AI techniques in wireless networks. With the emergence of diversified smart devices and applications, data held by individuals becomes more pervasive and nontrivial for publication. First, the data are more private and sensitive, as they cover every aspect of daily life, from the incoming data to the fitness data. Second, the publication of such data is also bandwidth‐consuming, as they are likely to be stored on mobile devices. The local differential privacy has been considered a novel paradigm for such distributed data publication. However, existing works mostly request the encoding of contents into vector space for publication, which is still costly in network resources. Therefore, this work proposes a novel framework for highly efficient privacy‐preserving data publication. Specifically, two sampling‐based algorithms are proposed for the histogram publication, which is an important statistic for data analysis. The first algorithm applies a bit‐level sampling strategy to both reduce the overall bandwidth and balance the cost among contributors. The second algorithm allows consumers to adjust their focus on different intervals and can properly allocate the sampling ratios to optimize the overall performance. Both the analysis and the validation of real‐world data traces have demonstrated the advancement of our work.

Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real‐time trajectory data and requests stronger spatial‐temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real‐time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real‐world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.

Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection. Corresponding mechanisms have been designed for multiple types of tasks, like the frequency estimation for categorical values and the mean value estimation for numerical values. However, the histogram publication of numerical values, containing abundant and crucial clues for the whole dataset, has not been thoroughly considered under this measurement. To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios. Therefore, this paper proposes a highly efficient framework for differentially private histogram publication of numerical values in a distributed environment. The proposed algorithms can efficiently adopt the correlations among multiple queries and achieve an optimal resource consumption. We also conduct extensive experiments on real‐world data traces, and the results validate the improvement of proposed algorithms.

The collection of high-confidence data has been one prominent step for many services in smart city systems. However, the privacy issues have been thwarting the seamless publication of data, especially as the data from different aspects of daily life may provide unprecedented coverage of contributors. Current solutions have been carefully designed to perturb or suppress the data before publication, so as to balance the privacy and utilities. However, they cannot fit the practice in smart cities, where multiple service providers request information on heterogeneous domains and regions of the city. Therefore, this article proposes a novel framework for data publication of workers in smart city systems. The framework allows workers and requestors to own and request various types of contents in different regions. The objective is to maximize the number of service providers receiving qualified utilities under privacy constraints. Furthermore, differential privacy is applied to guarantee that workers will not disclose personal information to requestors. In the technical part, the problem is proved to be NP-complete. Then two algorithms and strategies are proposed toward different cases: 1) workers apply identical privacy budgets for all published data and 2) workers are flexible on privacy settings. Both algorithms are theoretically analyzed on their performance of the released results. Finally, the evaluation of data sets of local businesses reveals that proposed algorithms can outperform baseline methods.

The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.

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.

In social networks, several influential individuals can promote an idea or a product to numerous individuals. Thus, it is valuable to solve the influence maximization (IM) problem, which asks for finding the most influential set of individuals in a social network. To estimate the influence of individuals, the existing independent cascade (IC) model simulates the influence diffusion only considering the influences from direct in-neighbors to nodes. This consideration does not hold in real life. In many cases, people are likely influenced by information depending on where it comes from, instead of who gives it. To simulate the influence diffusion more accurate, this paper proposes the second-order IC model, which takes the previous influence into consideration. In addition, we design an approximate algorithm and its distributed extension for IM under the second-order IC model. Experimental results show that our second-order IC model outperforms the IC model in terms of simulating influence diffusions. The proposed algorithms are efficient, and the obtained node sets are influential.

Due to the prominent development of public transportation systems, the taxi flows could nowadays work as a reasonable reference to the trend of urban population. Being aware of this knowledge will significantly benefit regular individuals, city planners, and the taxi companies themselves. However, to mindlessly publish such contents will severely threaten the private information of taxi companies. Both their own market ratios and the sensitive information of passengers and drivers will be revealed. Consequently, we propose in this paper a novel framework for privacy-preserved traffic sharing among taxi companies, which jointly considers the privacy, profits, and fairness for participants. The framework allows companies to share scales of their taxi flows, and common knowledge will be derived from these statistics. Two algorithms are proposed for the derivation of sharing schemes in different scenarios, depending on whether the common knowledge can be accessed by third parties like individuals and governments. The differential privacy is utilized in both cases to preserve the sensitive information for taxi companies. Finally, both algorithms are validated on real-world data traces under multiple market distributions.

The Internet of Things (IoT) has attracted the interests of both academia and industry and enables various real-world applications. The acquirement of large amounts of sensing data is a fundamental issue in IoT. An efficient way is obtaining sufficient data by the mobile crowdsensing. It is a promising paradigm which leverages the sensing capacity of portable mobile devices. The crowdsensing platform is the key entity who allocates tasks to participants in a mobile crowdsensing system. The strategy of task allocating is crucial for the crowdsensing platform, since it affects the data requester’s confidence, the participant’s confidence, and its own benefit. Traditional allocating algorithms regard the privacy preservation, which may lose the confidence of participants. In this paper, we propose a novel three-step algorithm which allocates tasks to participants with privacy consideration. It maximizes the benefit of the crowdsensing platform and meanwhile preserves the privacy of participants. Evaluation results on both benefit and privacy aspects show the effectiveness of our proposed algorithm.

Local business service systems (LBSS), such as Yelp and Dianping, play an essential role in making decisions like choosing a restaurant for our daily life. These systems heavily rely on individuals' voluntarily submitted reviews to build the reputation for nearby businesses. Unfortunately, the reviews expose users' private information such as visited places to the public and adversaries. Even worse, such location information is always public as it is the basic information of businesses, and adversaries could be anyone ranging from advertisement spammer to physical stalker. This paper formalizes the privacy preserving problem in local business service systems and propose a novel location privacy preserving framework. The framework can preserve users' location privacy in arbitrary local area and can maintain a good utility for both the system and every user. We evaluate our framework thoroughly towards real-world data traces. The results validate that the framework can achieve a good performance.

With the fast development of wireless technologies, wireless applications have invaded various areas in people's lives with a wide range of capabilities. Guaranteeing quality-of-service (QoS) is the key to the success of those applications. One of the QoS requirements, service frequency, is very important for tasks including multimedia transmission in the Internet of Things. A service frequency constraint denotes the length of the time period during which a link can transmit at least once. Unfortunately, it has not been well addressed yet. Therefore, this paper proposes a new framework to schedule multitransmitting flows in wireless networks considering service frequency constraint for each link. In our model, the constraints for flows are heterogeneous due to the diversity of users' behaviors. We first introduce a new definition for network stability with service frequency constraints and demonstrate that the novel scheduling policy is throughput-optimal in one fundamental category of network models. After that, we discuss the performance of a wireless network with service frequency constraints from the views of capacity region and total queue length. Finally, a series of evaluations indicate the proposed scheduling policy can guarantee service frequency and achieve a good performance on the aspect of queue length of each flow.

Due to the close correlation with individual's physical features and status, the adoption of cyber-physical social systems (CPSSs) has been inevitably hindered by users' privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs. We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results toward real datasets reveals that the performance is comparable to the global optimal one.

The huge volume of contents generated from mobile ends has dramatically contributed to big data. Unfortunately, current packet scheduling policies in wireless networks for the underlying big data delivery greatly hinder the utilization of the contents. Specifically, the delivery of real-time big data requires running-time interactions with users, as well as variable bandwidth consumptions to maintain fine user experience. There is no previous work designed for this kind of traffic. To mitigate this gap, a thorough-designed scheduling policy to assign and organize detailed packet transmissions for real-time big data is desired. This paper takes video delivery as a case study, which dominates the current real-time traffic and can be easily extended to other scenarios. We propose a novel scheduling policy, which assigns a proper number of video requests to servers and allocates bandwidth to these requests in a relatively small time scale. It helps with serving more users without compromising user experience of the current ones. We also prove that the scheduling policy has a guaranteed performance on the total number of served requests. Finally, the simulation results demonstrate that our scheduling policy outperforms other state-of-art methods significantly.

The past decade witnessed the dramatic evolution from Quality of Service (QoS) to Quality of Experience (QoE) in the design of wireless networks, especially on the aspect of link scheduling. In many applications, end users are concerned more about transmission quality of an individual task rather than the quality of a link, where a task may refer to a piece of music, video, etc. and may include many packets. This paper proposes a new network model aiming at improving user experience by pushing the scheduling problem to the task layer. A novel QoE requirement is designed to generalize the QoS requirements of a task, which is the ratio requirement. Following this design, a corresponding scheduling policy is proposed to capture it for each task and then reach an application-aware transmission allocation. We theoretically analyze the performance of the scheduling policy, and discuss the design of an optimal solution and the impact of the QoE requirements. Finally, the simulation results indicate that our scheduling policy can significantly improve QoE.

The pervasiveness of mobile applications stimulates more eager demand for Quality of Experience (QoE) than Quality of Service (QoS), especially on the aspect of link scheduling in wireless networks. In many applications, end users concern more about transmission quality of an individual task rather than an individual packet. A task may correspond to a piece of video, music, etc. And may include many packets. This paper proposes a new network model aiming at improving users' experience that pushes the scheduling problem to the task layer. We first introduce a QoE requirement that can generalize the QoS requirement in link scheduling, the partial result requirement. Subsequently, a novel scheduling policy is proposed which can capture this requirement for each task, and then performs an application-aware scheduling. We theoretically analyze the performance of the novel scheduling policy, and discuss the impact of the QoE requirements and network settings. Finally, the simulation results indicate that our scheduling policy can significantly improve QoE.

A thorough understanding of capacity of wireless networks can help with effective design and efficient employment of wireless networks. Much effort has been spent on investigating capacity of multicast which is a popular communication model and generalization of unicast and broadcast. However, most previous works assume homogeneous traffic patterns, which is not meaningful for practical applications. This paper analyzes the capacity of wireless networks with multiple types of multicast sessions without the assumption of homogeneous traffic patterns. A new network model is proposed accommodating practical traffic patterns and the capacity is analyzed accordingly. A theoretical upper bound is derived, and a feasible transmission scheme with capacity lower bound is presented. Two bounds are asymptotically tight, that is, in the order of Θ(a2ns/∑i=1nsmin√ki⋅ Ri⋅ r,Ri2⋅ W), where a is the side length of the deployed region, r is the transmission range, ns is the number of multicast sessions, and ki and Ri are parameters of multicast session i. Furthermore, the variation of capacity towards different numbers of and distributions of destinations is illustrated.