Membership inference attacks have been proposed to infer whether a specific sample is in the training dataset of a victim model. Inferred membership may reveal sensitive information, e.g., personal health condition deduced from a disease prediction model. In federated learning where local datasets of different participants are supposed to be protected, membership inference attacks may still pose a privacy threat. However, existing membership inference attacks in federated learning select the final epoch and random intermediate epochs during training to solicit features for the attack, which may lead to poor attack performance. In this paper, we propose a novel membership inference attack in federated learning, which attempt to find the key epoch during training that is most indicative of the differences between member and non-member samples. Inspired by the overfitting phenomena that often occurs during the training of learning models, we derive the key epoch as the onset of overfitting. We design efficient algorithms to pinpoint the key epoch and leverage attention mechanism to weight the importance of different features. We evaluate our scheme on four datasets and compare experimental results with state-of-the-art attack algorithms. Experimental results demonstrate that our attack outperforms existing works in terms of inference accuracy.

Social status, the social influence of a user, plays an important role in many real-world applications, e.g., trust relations and information propagation in a social network. In this paper, we reveal the possibility of falsifying social status through adversarial attacks in graph neural networks (GNNs). Different from neural networks in the visual or speech domain, GNNs take the attributes of nodes and edges in a graph as features. To cater to the characteristics of GNNs,$\vphantom {_{\int }}$we design a new paradigm of adversarial example attack, named$A^{2} S^{2}$- GNN ($\mathbf {GNN}$-based$\mathbf {A}$dversarial$\mathbf {A}$ttacks on$\mathbf {S}$ocial$\mathbf {S}$tatus), aiming at manipulating the social status of a target node in social networks. The key idea is to establish relationships or break relationships between a set of compromised nodes and the target node. More specifically, we consider a signed directed graph representing complicated positive/negative asymmetric relationships between nodes. We design an efficient adversarial attack algorithm to determine the minimum set of signed links that should be created or deleted to reach the attack objective. We conduct extensive experiments on baseline datasets. Compared with the benchmark algorithms,$A^{2} S^{2}$- GNN can effectively promote or vilify the social status of the target node up to 89.36% and 192.38%, respectively, while keeping the modification to the social network to the minimum. Furthermore, the experimental results on six status evaluating algorithms verify the transferability of our proposed attack algorithm.

Age-of-Information (AoI) has been recently reckoned as a suitable parameter to evaluate the freshness of collected information, which is essential for data retrieval in Internet of Things, especially the monitoring tasks, e.g., the operating situation of equipments. To motivate a large number of sensor nodes and solicit more up-to-date information from these nodes, the control center usually allocates rewards to nodes according to their proportional contributions. This induces intense competitions among nodes who try to gain high payoffs by carefully balancing the rewards and the costs. In this article, we propose a novel stochastic game model to formulate the competition among sensor nodes, which considers AoI as a metric used by the control center to quantify the contributions of nodes. We also take into account the uncertainty of channel quality, which affects the transmission success ratio of packets generated by nodes. Finally, we design an ϵ-Nash learning algorithm, which adopts the θ-greedy exploration strategy, to derive the ϵ-approximate Nash equilibrium such that nodes can maximize their long-term payoffs. Our substantive simulation results and analysis verify that the proposed algorithm outperforms baseline algorithms in bringing higher payoffs to nodes and more fresh information to the control center.

With recent advances in the study of biometrics, gait analysis has drawn much attention for its potential use in forensics, surveillance, and legal systems. In this paper, we present WIAGE, a contactless and non-intrusive gait-based age estimation system, which leverages wireless sensing to perform gait analysis to infer the age of individuals. Traditional age estimation systems either require users to carry wearable devices that are inconvenient or rely on image processing that is computationally intensive and sensitive to lighting conditions and occlusion. In contrast, WIAGE utilizes the incumbent WiFi infrastructure to infer the age of users with minimal interference to their activities. We adopt a series of signal processing techniques to recover clear gait patterns from the noisy WiFi signals and extract the most relevant features from steps that can be used for robust age estimation. The experimental results show that WIAGE can achieve an age estimation accuracy of 95.2% for 23 users, which demonstrates the feasibility and effectiveness of our proposed system.

Crowdsourcing has become a popular paradigm to leverage the collective intelligence of massive crowd workers to perform certain tasks in a cost-effective way. Task assignment is an essential issue in crowdsourcing platforms owing to heterogeneous tasks and work skills. In this article, we focus on assigning workers with diversified skill levels to crowdsourcing tasks with different quality requirements and budget constraints. Task assignment is fundamentally a many-to-one matching problem, where one task is allocated to multiple users who can meet the minimum quality requirement of the task within the limited budget. While most existing works try to maximize the utility of the crowdsourcing platform, we take into account the individual preferences of crowdsourcers and workers toward each other to ensure the stability of task assignment results. In this article, we propose task assignment mechanisms that can guarantee stable outcomes for the many-to-one matching problem with lower and upper bounds (i.e., quality requirement and budget constraint) in regard to heterogeneous worker skill levels. Extensive simulation results show that the proposed algorithms can greatly improve the success ratio of task accomplishment and worker happiness compared with existing algorithms.

Influence maximization in social networks is of great importance for marketing new products. Signed social networks with both positive (friends) and negative (foes) relationships pose new challenges and opportunities, since the influence of negative relationships can be leveraged to promote information propagation. In this paper, we study the problem of influence maximization for advertisement recommendation in signed social networks. We propose a new framework to characterize the information propagation process in signed social networks, which models the dynamics of individuals' beliefs and attitudes towards the advertisement based on recommendations from both positive and negative neighbours. To achieve influence maximization in signed social networks, we design a novel Signed-PageRank (SPR) algorithm, which selects the initial seed nodes by jointly considering their positive and negative connections with the rest of the network. Our extensive experimental results confirm that our proposed SPR algorithm can effectively and efficiently influence a broader range of individuals in the signed social networks than benchmark algorithms on both synthetic and real datasets.

Nowadays, smartphones have become indispensable for people and many users may browse information on their smartphones during walking. This raises severe concerns about pedestrian safety when distracted users inadvertently walks from the sidewalk onto the street. In this paper, we present WatchOut, a convenient pedestrian alerting system based on fine-grained step classification (e.g., flat, up and down the ramp) and event detection (e.g., entering the street, turn) in an urban environment. In contrast to existing systems that rely on shoe-mounted sensors, Watchout leverages rich sensors on the smartphone without requiring any additional hardwares. With carefully designed realtime data processing and classification algorithms, WatchOut can achieve a high detection accuracy. We develop a fully-functional Android application of WatchOut with user-friendly interface that will issue timely alerts to warn users of unsafe conditions. Extensive experiments with 23 volunteers in real pedestrian environment have confirmed the effectiveness of WatchOut, which achieves a comparable detection accuracy as the shoe-mounted system with off-the-shelf smartphones.

Social tie prediction is an important issue in social network analysis. Transfer learning is often used for social tie prediction to address the problem of insufficient labeled training data, since few users manually annotate their social relationships. In this paper, we propose TranGAN, a novel generative adversarial network (GAN) based transfer learning framework for social tie prediction, which leverages social theories as the common knowledge to bridge the source network and the target network. GAN helps augment the original data set by generating data samples that have a similar probability distribution to that of the original data, and the training of TranGAN converges faster compared to existing transfer learning models. We evaluate the performance of TranGAN with extensive experiments, and show that TranGAN outperforms traditional learning algorithms and existing transfer learning algorithm on several metrics, and is efficient for large-scale social networks.

IPTV provides a variety of services to users, e.g., Live TV, Video on Demand (VOD), and Catch-up TV, which allows users to freely select from a huge pool of program genres. A high-quality program recommendation system that can well predict users' dynamic preferences is desirable to improve user satisfaction. In this paper, we make the first attempt to design a personalized TV program recommendation system based on tensor learning that leverages temporal context information and implicit feedback. We establish the user preference model by carefully analyzing the influence of program genres and time context on users' viewing behavior in IPTV viewing logs. To obtain the optimal program recommendation, we adopt tensor decomposition to mine the latent relationship among users, program genres and the time contexts, while address issues of data sparsity and missing information. Our extensive experimental results on a real dataset from a major IPTV service provider in China have confirmed the superiority of our proposed recommendation algorithm over baseline algorithms in achieving a higher recommendation accuracy and covering a wider range of programs.

To enable dynamic spectrum access, service providers with spare spectrum (sellers) trade with those who are in need of additional spectrum (buyers). In a spectrum market, the transaction result is essentially a matching between sellers and buyers. Though it is tempting to optimize the matching over certain utility functions, a stable matching is more desirable, since no participants have incentives to deviate from the matching result. Existing spectrum matching algorithms only consider the maximum number of channels a buyer can purchase, but ignore minimum spectrum requirement that is essential to support proper operation of wireless communications. In this paper, we present a new framework of spectrum matching with both maximum quota and minimum requirements. Different from conventional matching problems, the spectrum market poses distinctive challenges due to spectrum reusability. To tackle this problem, we design two novel algorithms that satisfy different stability criterion: Extended Deferred Acceptance (EDA) algorithm that is fair but wasteful and the Multistage Deferred Acceptance (MDA) algorithm that is non-wasteful but weakly fair. Both algorithms converge to an interference-free matching and guarantees the minimum spectrum requirement. The simulation results show that the two proposed algorithms can raise buyer happiness and the channel utilization.

In crowdsourcing systems, job assignment is one of the fundamental concerns. Existing works address the job assignment problem solely from the perspective of the crowdsourcer, aiming at maximizing the utility or minimizing the cost for the crowdsourcer. In this paper, we take into consideration users' preferences towards different jobs, and propose a novel matching framework for job assignment in crowdsourcing systems. Assigning multiple users to the same job will improve its quality, however, the crowdsourcer has to guarantee that the total payment to these users is less than the budget of this job. As users ask for different payments due to heterogeneity in their quality levels, classic deferred acceptance algorithm cannot reach a stable job assignment. In this paper, we formulate the job assignment problem in crowdsourcing systems as a many-to-one matching with budget constraints. Then, we design an algorithm that produces a stable job assignment in spite of user heterogeneity. Simulation results show that the proposed job assignment algorithm yields a high average job quality with a low computational complexity.

Crowdsourcing leverages the collective intelligence of the massive crowd workers to accomplish tasks in a cost-effective way. On a crowdsourcing platform, it is challenging to assign tasks to workers in an appropriate way due to heterogeneity in both tasks and workers. In this paper, we explore the problem of assigning workers with various skill levels to tasks with different quality requirements and budget constraints. We first formulate the task assignment as a many-to-one matching problem, in which multiple workers are assigned to a task, and the task can be successfully completed only if a minimum quality requirement can be satisfied within its limited budget. Different from traditional task assignment mechanisms which focus on utility maximization for the crowdsourcing platform, our proposed matching framework takes into consideration the preferences of individual crowdsourcers and workers towards each other. We design a novel algorithm that can generate a stable outcome for the many-to-one matching problem with lower and upper bounds (i.e., quality requirement and budget constraint), as well as heterogeneous worker skill levels. Through extensive simulations, we show that the proposed algorithm can greatly improve the success ratio of task accomplishment and worker happiness, when compared with existing algorithms.

To enable dynamic spectrum access, service providers with spare spectrum (sellers) trade with those who are in need of additional spectrum (buyers). In a spectrum market, the transaction result is essentially a match between sellers and buyers. Though it is tempting to optimize the matching over certain utility functions, a stable matching is more desirable, since it takes into account a diverse set of preferences of buyers and sellers, and produces a matching result which no participants have incentives to deviate from. While existing works on spectrum matching only consider the maximum number of channels a buyer can purchase, in real-world scenarios, the minimum spectrum requirement should be satisfied to support the proper operation of wireless communication. To address this issue, in this paper, we present a new framework of spectrum matching with both maximum and minimum requirements. Different from conventional matching problems, the spectrum market poses distinctive challenges due to spectrum reusability. Instead of being sold exclusively to just one buyer, the same channel can be reused by multiple buyers who are not interfering with each other. To tackle this problem, we design a novel algorithm, called Extended Deferred Acceptance (EDA), that converges to an interference-free matching and guarantees the minimum spectrum requirement. We theoretically prove the stability of the matching result. Our simulation results show that EDA can achieve a 100% coverage on the minimum requirements, while alternative benchmark algorithms fail to do so, and buyers are more satisfied with the matching result of EDA than that of alternative algorithms.

In this paper, we propose a novel clock calibration approach, which addresses two key challenges for clock calibration in Wireless Sensor Networks: excessive communication overhead and the trade-off between accuracy and cost. To achieve this, our approach leverages the fact that the clock skew is highly correlated to temperature, which can serve as both an assistant for clock skew estimation and a regulatory factor for the duty-cycled design. Our approach is one order of magnitude more power-efficient than communication based approaches since the calibration largely relies on local temperature information. In addition, our approach provides a nice feature of self-adaptive period, which can substantially promote the system flexibility. We present the theory behind our approach, and provide preliminary results of a simulated comparison of our approach and some recent approaches.

Microeconomics-inspired spectrum auctions can effectively improve the spectrum utilization for wireless networks to satisfy the ever increasing service demands. Considering the spatial reuse, the bidding nodes without mutual interference are grouped as virtual bidders competing for the spectrum bands, which turns a multi-winner spectrum auction into a traditional single-winner auction. To make the participating nodes bid truthfully, strategy-proof auctions are exploited to allocate the vacant spectrum bands. However, how to fairly allocate the profits of the virtual bidder among the winning bidders is still an imperative problem to solve. In this paper, we propose a shapley value based profit allocation (SPA) to distribute the profit among the bidding nodes according to their marginal contributions, which are both from helping the virtual bidder to win the auction and from generating the revenue during the auction period. Our simulation and analysis show that SPA can effectively integrate the contributions from the two stages in the spectrum auction and fairly allocate the profit among the winning bidders.

The event-driven nature of wireless sensor networks (WSNs) leads to unpredictable network load. As a result, congestion may occur at sensors that receive more data than they can forward, which causes energy waste, throughput reduction and packet loss. In this paper, we propose a rate-based fairness-aware congestion control protocol (FACC), which controls congestion and achieves approximately fair bandwidth allocation for different flows. In FACC, we categorize intermediate relaying sensor nodes into near-source nodes and near-sink nodes. Near-source nodes maintain per-flow state, and allocate an approximately fair rate to each passing flow. On the other hand, near-sink nodes do not need to maintain per-flow state and use a light-weight probabilistic dropping algorithm based on queue occupancy and hit frequency. Our simulation results and analysis show that FACC provides better performance than previous approaches in terms of throughput, packet loss, and fairness.