Federated Multi-View Clustering has gained increasing attention for its ability to discover complementary clustering structures of distributed multi-view data while preserving data privacy. However, real-world clients often only have access to partial views, and the view incompleteness poses great challenges to federated multi-view feature fusion to exploit consistent and complementary information. Moreover, efficiency is highly expected in federated scenarios due to the limited resources of each client. To alleviate these issues, we propose Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint (FIMVC-TLRC), which incorporates anchors to improve efficiency and is able to address prevalent view incompleteness issue in federated scenarios. FIMVC-TLRC aligns the local anchor graphs and employs a tensorized low-rank constraint based on the tensor Schatten p-norm to enforce the consistency of the data representations learned by each client. Besides, a federated optimization framework is developed to jointly optimize the construction and alignment of anchor graphs, thus enabling collaborative and privacy-preserving training. Experimental results on multiple datasets demonstrate its effectiveness.

Marginal Fisher Analysis (MFA) is a classical dimensionality reduction (DR) method that leverages dual graphs to capture intra-class compactness and inter-class separability. However, MFA’s reliance on high-quality labels limits its practical application. For another, existing unsupervised DR methods neglect data’s local manifold relationship, resulting in poor discriminativeness. To address these limitations, we propose a novel DR method named Discriminative Graph Embedding Framework (DGEF) via Label-Free Marginal Fisher Analysis. Our approach uses the adjacency matrix and cluster indicator matrix derived from centerless K-Means to construct intrinsic graph and penalty graph, which preserve the local manifold structure of the data. Additionally, we have derived the convertible relationship between centerless K-Means and Manifold learning and unified them within a graph embedding framework. By adopting the intrinsic graph and penalty graph, our DGEF avoids centroid initialization and ensures robustness and discriminativeness. This method achieves dimensionality reduction adaptively without relying on labeled data. Extensive experiments on benchmark datasets show that our approach outperforms conventional methods in clustering performance.

Fair incomplete multi-view clustering (FIMVC) confronts a critical yet unresolved challenge, as existing methods often fail to address the intertwined issues of data missingness and algorithmic bias simultaneously. In this paper, we propose a novel FIMVC method named Adversarial Fair Incomplete Multi-View Clustering (AFIMVC). The core of AFIMVC is a new adaptive adversarial disentanglement mechanism. This mechanism trains the feature encoder to produce representations that are invariant to sensitive attributes by adversary learning, where the adversarial intensity is dynamically controlled by the model's real-time bias. Additionally, we develop a probabilistic cross-view contrastive learning strategy to achieve semantic consistency in latent space. To handle missing data, AFIMVC employs a context-aware fusion strategy that leverages cross-sample attention to robustly synthesize a unified representation from incomplete views. Extensive experiments demonstrate that AFIMVC achieves a state-of-the-art balance between clustering accuracy and fairness, significantly outperforming existing methods.

With the widespread adoption of mobile devices, an increasing number of users are engaging in social interactions through these devices. Mobile social networks have thus emerged in response. Existing research has shown that utilizing mobile social relations can effectively enhance the performance of recommendation systems. However, most studies only exploit single social relations such as pairwise relations, overlooking the effect of high-order complexity of user relations which contain some potentially beneficial information. What's more, they ignore the impact of the trusters who provide some potential feedbacks in the mobile social network. Therefore, this paper proposes our framework H2TRec using hypergraph convolution in the pretraining stage to learn high-order neighbor information in the mobile social network and utilizing the metapath-based GAT to model users' bidirectional trust relations. First, mobile social communities are partitioned by random walk based on the fusion graph which consolidates all different nodes and relations. Second, each mobile social community is represented as a hyperedge to construct the hypergraph and the high-order neighbor prior knowledge is learned using hypergraph convolution. Third, implicit relations are mined and the metapath-based GAT is utilized to model the preferences of users and items. Notably, unlike previous work, we consider mobile users' out-degree and in-degree features, which enhance the user embeddings. Additionally, a loss term aiming to improve centrality is added to make the preference features of mobile social communities more prominent. Extensive experiments on five popular real-world datasets demonstrate that our H2TRec can improve precision compared with state-of-the-art methods. We release the source code athttps://github.com/kangkang-yun/H2TRec.

Locality Preserving Projections (LPP) aims to find a projection matrix to map the high-dimensional data into a low-dimensional subspace while preserving the local manifold structure, which is a classical unsupervised subspace learning method. However, the lack of label guidance makes LPP not able to fully exploit the discriminative information of the data. To solve the problem, we propose a Self-Guided Discriminative LPP algorithm employing pseudo labels learned by K-Means to guide the subspace learning. In this way, it facilitates the discovery of discriminative cluster information while preserving inherent manifold structure. Besides, considering K-Means' sensitivity to selection of cluster centroids, we introduce a centerless K-Means method to improve robustness by eliminating the need of centroid initialization. We also discuss the internal relationship between K-Means and LPP, and prove that K-Means can be written in the form of LPP under certain conditions. Experiments on seven benchmark datasets demonstrate that our method greatly improves the clustering performance.

Current deep multi-modal graph clustering methods primarily rely on Graph Neural Network (GNN) to fully exploit attribute features and graph structures, including message propagation and low-dimensional feature embedding. However, these methods lack further exploration of graph structural information, such as the relationship between nodes and shortest paths. Additionally, they may not sufficiently mine complementary information among multi-modal graph data. To address these issues, we propose a novel Deep Multi-modal Graph Clustering via Graph Transformer Network method, called DMGC-GTN. This method thoroughly dissects and utilizes graph structural information, applying graph smoothing to node features and incorporating various forms of embeddings into the transformer architecture. This achieves a unified embedding of graph structure and multi-modal feature attributes, fully exploiting the complementary information within multi-modal graph data. Extensive experiments demonstrate the effectiveness of our algorithm.

Multi-view clustering (MVC) methods have garnered considerable attention within centralized data frameworks. However, real-world multi-view data are often collected and stored by different organizations, complicating the practical deployment of MVC and motivating the emergence of federated multi-view clustering (FMVC). Existing FMVC approaches typically necessitate post-processing to derive clustering labels and confront challenges in effectively exploring the complementary and consistent information across multi-view data residing in different entities. To address these limitations, we propose a novel framework termed Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization (SFOMVC-TR). This framework facilitates one-step clustering at each client and employs tensor learning to capture consistent and complementary information through a centralized server. Additionally, it adopts anchor graphs to enhance clustering efficiency and scalability in high-dimensional data. By incorporating a Lp,q sparse regularization on the projection matrix, SFOMVC-TR enables the direct projection of anchors into clustering assignments to mitigate redundancy. A federated optimization framework is developed to support collaborative and privacy-preserving training under the coordination of the server. Extensive experiments on multiple datasets validate the privacy and effectiveness of our method.

Multi-view clustering aims to identify consistent and complementary information across multiple views to partition data into clusters, emerging as a popular unsupervised method for multi-view data analysis. However, existing methods often design view-specific encoders to extract distinct features from each view, lacking exploration of their complementarity. Additionally, current contrastive-based multi-view clustering methods may lead to erroneous negative sample pairs conflicting with the clustering objective. To address these challenges, we propose a novel Contrastive Multi-view Subspace Clustering via Tensor Transformers Autoencoder (TTAE). On the one hand, it facilitates information exchange between views by tensor transformers autoencoder, thereby enhancing complementarity. On the other hand, It learns a consistent subspace with a self-expression layer. Meanwhile, adaptive contrastive learning helps to provide more discriminative features for the self-expression learning layer, and the self-expression learning layer in turn supervises contrastive learning. Moreover, our method adaptively selects positive and negative samples for contrastive learning to mitigate the impact of inappropriate negative sample pairs. Extensive experiments on several multi-view datasets demonstrate the effectiveness and superiority of our model.

Linear Discriminant Analysis (LDA) is a classical supervised dimensionality reduction algorithm. However, LDA focuses more on global structure and overly depends on reliable data labels. For data with outliers and nonlinear structures, LDA cannot effectively capture the true structure of the data. Moreover, the subspace dimension learned by LDA must be smaller than cluster number, which limits its practical applications. To address these issues, we propose a novel unsupervised LDA method that combines centerless K-means and LDA. This method eliminates the need to calculate cluster centroids and improves model robustness. By fusing centerless K-means and LDA into a unified framework and deducing the connection between K-means and manifold learning, this method captures the local manifold structure and discriminative structure. Additionally, the dimensionality of the subspace is not restricted. This method not only overcomes the limitations of traditional LDA but also improves the model’s adaptability to complex data. Extensive experiments on seven datasets demonstrate the effectiveness of the proposed method.

Federated Multi-View Clustering (FedMVC) aims to uncover consistent clustering structures from distributed multi-view data for clustering while preserving data privacy. However, existing FedMVC methods under vertical settings either ignore the ubiquitous incomplete view issue or require uploading data features, which may lead to privacy leakage or induce high communication costs. To mitigate the view incompleteness issue and simultaneously maintain privacy and efffciency, we propose a novel Federated Multiview Graph Clustering with Incomplete Attribute Imputation (FMVC-IAI). This method constructs a consensus graph structure through complementary multi-view data and then utilizes a non-parametric graph neural network (GNN) to impute missing features. Additionally, it utilizes the adjacency graph as the knowledge carrier to share and fuse the multi-view information. To alleviate the high communication cost due to graph sharing, we proposed to share the anchor graph for global adjacency graph construction, which reduces communication cost and also helps to reduce privacy leakage risk. Extensive experiments demonstrate the superiority of our method in FedMVC tasks with incomplete views.

Multi-view clustering (MVC) has emerged as an important unsupervised multi-view learning method that leverages consistent and complementary information to enhance clustering performance. Recently, tensorized MVC, which processes multi-view data as a tensor to capture their cross-view information, has received considerable attention. However, existing tensorized MVC methods generally overlook deep structures within each view and rely on post-processing to derive clustering results, leading to potential information loss and degraded performance. To address these issues, we develop Tensorial Multi-view Clustering with Deep Anchor Graph Projection (TMVC-DAGP), which performs deep projection on the anchor graph, thus improving model scalability. Besides, we utilize a sparsity regularization to eliminate the redundancy and enforce the projected anchor graph to retain a clear clustering structure. Furthermore, TMVC-DAGP leverages weighted Tensor Schatten $p$-norm to exploit the consistent and complementary information. Extensive experiments on multiple datasets demonstrate TMVC-DAGP's effectiveness and superiority.

Incomplete multi-view clustering (IMVC) extracts consistent and complementary information from multi-source/modality data with missing views, aiming to partition the data into different clusters. It can effectively address the problem of unsupervised multi-source data analysis in complex environments and has gained considerable attention. However, the fairness of IMVC remains underexplored, particularly when data contains sensitive features ({e.g.}, gender, marital status, and age). To tackle the problem, this work presents a novel Fair Incomplete Multi-View Clustering (FIMVC) method. The proposed FIMVC introduces fairness constraints to ensure clustering results are independent of sensitive features. Additionally, it learns consensus representations to enhance clustering performance by maximizing mutual information and aligning the distributions of different views. Experimental results on three datasets containing sensitive features demonstrate that our method improves the fairness of clustering results while outperforming state-of-the-art IMVC methods in clustering performance.

As a key infrastructure for social fintech ecosystems, ethereum enables decentralized finance (DeFi) applications where security issues directly compromise ecosystem stability. Among critical security concerns, ethereum phishing scams stand as typical scams. Criminals employ distinctive fund transfer patterns (e.g., money laundering stages: placement, layering, and integration) to obscure illicit funds through long transaction paths. While graph neural networks (GNNs) dominate detection methods, they fail to model these long paths effectively. To address this, we propose the first framework to detect phishing scams through explicitly modeling fund transfer patterns. Our novel method, IMPUTATION, introduces: 1) a heuristic fund transfer path graph construction method utilizing iterative transaction pairing to capture complicated fund transfer patterns; 2) role-topology account embeddings encoding fund transfer patterns; 3) attention fusion leveraging initial transactions to suppress path noise; and 4) heterogeneous correlation graphs with weighted adjacency reconstruction modeling interpath dependencies. Extensive experiments demonstrate that IMPUTATION outperforms on all five metrics and detecting Ethereum phishing scams from fund transfer patterns is effective.

Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of Information Retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multi-modal perspective with promising progress, they still fail to fully leverage the interactions between multi-modal information or ignore the item–item contextual connectivities of intra-outfit. In this article, a novel FCM scheme is proposed based on Correlation-Aware Cross-Modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multi-modal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multi-modal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item–item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation.

Partial multi-view clustering is a challenging and practical research problem for data analysis in real-world applications, due to the potential data missing issue in different views. However, most existing methods have not fully explored the correlation information among various incomplete views. In addition, these existing clustering methods always ignore discovering discriminative features inside the data itself in this unsupervised task. To tackle these challenges, we propose Partial Multi-View Clustering via Self-Supervised \textbf{N}etwork (PVC-SSN) in this paper. Specifically, we employ contrastive learning to obtain a more discriminative and consistent subspace representation, which is guided by a self-supervised module. Self-supervised learning can exploit effective cluster information through the data itself to guide the learning process of clustering tasks. Thus, it can pull together embedding features from the same cluster and push apart these from different clusters. Extensive experiments on several benchmark datasets show that the proposed PVC-SCN method outperforms several state-of-the-art clustering methods.

Multi-view clustering (MVC) methods based on non-negative matrix factorization (NMF) have gained popularity owing to their ability to provide interpretable clustering results. However, these NMF-based MVC methods generally process each view independently and thus ignore the potential relationship between views. Besides, they are limited in the ability to capture nonlinear data structures. To overcome these weaknesses and inspired by deep learning, we propose a multi-view clustering method based on deep non-negative tensor factorization (MVC-DNTF). With deep tensor factorization, our method can well exploit the spatial structure of the original data and is capable of extracting more deep and nonlinear features embedded in different views. To further extract the complementary information of different views, we adopt the weighted tensor Schatten p-norm regularization term. An optimization algorithm is developed to effectively solve the MVC-DNTF objective. Extensive experiments are performed to demonstrate the effectiveness and superiority of our method.

Multi-view clustering has emerged as an important unsupervised method to process unlabelled multi-view data that provides a comprehensive description of an object. Existing multi-view clustering methods focus on centralized settings but ignore the fact that real-world multi-view data may be distributed across different entities. The sensitive information embedded in multi-view data hinders the cooperative training of multi-view clustering, since data of different views cannot be directly shared, leading to a great challenge to cooperatively exploit the consistent and complementary information of different views. To validate the multi-view clustering in distributed scenarios, in this paper, we propose a novel federated multi-view method named Federated Multi-View Fuzzy C-means with Schatten-p Norm Minimization (FMVFCMSP) which is based on fuzzy C-means and tensor Schatten p-norm. Specifically, we utilize the membership degrees to replace conventional hard clustering assignment in K-means, enabling improved uncertainty handling and less information loss. Moreover, we introduce a tensor Schatten p-norm-based regularizer to fully explore the inter-view complementary information and global spatial structure. We also develop a federated optimization algorithm enabling clients to collaboratively learn the clustering results. Extensive experiments on several datasets demonstrate that our proposed method exhibits superior performance in federated multi-view clustering.

Fair multi-view clustering aims to achieve both satisfactory clustering performance and non-discriminatory outcomes with respect to sensitive attributes. Existing fair multi-view clustering methods impose a constraint that requires the distribution of sensitive attributes to be uniform within each cluster. However, this constraint can lead to misallocation of samples with sensitive attributes. To solve this problem, we propose a novel Deep Fair Multi-View Clustering (DFMVC) method that learns a consistent and discriminative representation instructed by a fairness constraint constructed from the cluster distribution. Specifically, we incorporate contrastive constraints on semantic features from different views to obtain consistent and discriminative representations for each view. Additionally, we align the distribution of sensitive attributes with the target cluster distribution to achieve optimal fairness in clustering results. Experimental results on four datasets with sensitive attributes demonstrate that our method improves fairness and clustering performance compared with state-of-the-art multi-view clustering methods.

Mobile Crowdsourcing (MCS) is a widely adopted data collection method that utilizes ubiquitous mobile devices as sensors. The centralized nature of traditional MCS introduces certain weaknesses, leading to a growing interest in blockchainbased MCS. Although decentralized MCS proves to be effective, it also presents several privacy and trust concerns. Specifically, ensuring node trust authentication in an anonymous and decentralized manner is critical due to the susceptibility of a trustworthy and decentralized MCS platform to various attacks. Nevertheless, the absence of a centralized and trusted entity in blockchain poses significant challenges in managing node keys for authentication. Moreover, trust often conflicts with privacy, and the transparency of blockchain further complicates the achievement of anonymous authentication while preserving privacy. To address these challenges, we propose a scheme for decentralized anonymous authentication based on threshold and partial blind signature, which provides a key alternation service. The proposed scheme enables trust credential issuance without disclosing the linkable relationship between the public keys before and after the alternation. Extensive analyses and experiments are conducted to demonstrate the security and efficiency of the proposed scheme.

Cross-view subspace clustering has become a popular unsupervised method for cross-view data analysis because it can extract both the consistent and complementary features of data for different views. Nonetheless, existing methods usually ignore the discriminative features due to a lack of label supervision, which limits its further improvement in clustering performance. To address this issue, we design a novel model that leverages the self-supervision information embedded in the data itself by combining contrastive learning and self-expression learning, i.e., unsupervised cross-view subspace clustering via adaptive contrastive learning (CVCL). Specifically, CVCL employs an encoder to learn a latent subspace from the cross-view data and convert it to a consistent subspace with a self-expression layer. In this way, contrastive learning helps to provide more discriminative features for the self-expression learning layer, and the self-expression learning layer in turn supervises contrastive learning. Besides, CVCL adaptively chooses positive and negative samples for contrastive learning to reduce the noisy impact of improper negative sample pairs. Ultimately, the decoder is designed for reconstruction tasks, operating on the output of the self-expressive layer, and strives to faithfully restore the original data as much as possible, ensuring that the encoded features are potentially effective. Extensive experiments conducted across multiple cross-view datasets showcase the exceptional performance and superiority of our model.

Multi-view graph clustering aims to leverage different views to obtain consistent information and improve clustering performance by sharing the graph structure. Existing multi-view graph clustering algorithms generally adopt a single-pathway network reconstruction and consistent feature extraction, building on top of auto-encoders and graph convolutional networks (GCN). Despite their promising results, these single-pathway methods may ignore the significant complementary information between different layers and the rich multi-level context inside. On the other hand, GCN usually employs a shallow network structure (2-3 layers) due to the over-smoothing with the increase of network depth, while few multi-view graph clustering methods explore the performance of deep networks. In this work, we propose a novel Dropping Pathways strategy toward building a deep Multi-view Graph Subspace Clustering network, namely DPMGSC, to fully exploit the deep and multi-level graph network representations. The proposed method implements a multi-pathway self-expressive network to capture pairwise affinities of graph nodes among multiple views. Moreover, we empirically study the impact of a series of dropping methods on deep multi-pathway networks. Extensive experiments demonstrate the effectiveness of the proposed DPMGSC compared with its deep counterpart and state-of-the-art methods.

Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outlier and heavy-tailed noises. Understanding modal regression's theoretical behavior can be fundamental in learning theory. Despite significant progress in characterizing its statistical property, the majority results are based on the assumption that samples are independent and identical distributed (i.i.d.), which is too restrictive for real-world applications. This paper concerns about the statistical property of regularized modal regression (RMR) within an important dependence structure - Markov dependent. Specifically, we establish the upper bound for RMR estimator under moderate conditions and give an explicit learning rate. Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of the underlying Markov chain. This result shed a new light on characterizing the theoretical underpinning for robust regression.

Mobile crowdsourcing (MCS) has become an effective data collection method due to its mobility, low cost, and flexibility. However, since centralized MCS confronts severe security and privacy risks in reality, many researchers are devoted to building a decentralized MCS system based on blockchain. Despite the effectiveness of these schemes, they fail to offer anonymous authentication on the trust of MCS nodes, although privacy is a main concern in MCS and trust plays an important role in a series of MCS activities, such as worker selection and truth discovery. Nevertheless, anonymous authentication on trust is not a trivial issue since trust evaluation usually conflicts with anonymity, which is a necessary privacy requirement in an open MCS environment. To tackle this problem, we leverage Intel software guard extension (SGX) and propose a scheme to anonymously authenticate trust with trustworthy trust evaluation in a blockchain-based MCS system. The scheme employs an SGX-enabled cloud server to periodically alter user public/private key pairs and mix newly altered keys among a number of faked keys in order to ensure unlinkability. Besides, we consider the unique features of MCS and work out a novel trust evaluation method by aggregating both subjective feedback and objective behaviors. Finally, we conduct several analyses and experiments to illustrate its security and efficiency.

Pervasive Social Networking (PSN) supports online and instant social activities with the support of heterogeneous networks. Since reciprocal activities among both familiar/unfamiliar strangers and acquaintances are quite common in PSN, it is essential to offer trust information to PSN users. Past work normally evaluates trust based on a centralized party, which is not feasible due to the dynamic changes of PSN topology and its specific characteristics. The literature still lacks a decentralized trust evaluation scheme in PSN. In this article, we propose a novel blockchain-based decentralized system for trust evaluation in PSN, called Social-Chain. Considering mobile devices normally lack computing resources to process cryptographic puzzle calculation, we design a lightweight consensus mechanism based on Proof-of-Trust (PoT), which remarkably improves system effectivity compared with other blockchain systems. Serious security analysis and experimental results further illustrate the security and efficiency of Social-Chain for being feasibly applied into PSN.

Abstract The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), mobile crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional wireless sensor networking, MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy, and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving, and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions.

Pervasive social networking (PSN) supports instant social activities anywhere and at any time with the support of heterogeneous networks. In order to preserve privacy and achieve trustworthy PSN, anonymous authentication on node trust is expected in PSN. However, the literature still lacks serious studies on this issue. In this paper, we propose an anonymous authentication scheme for authenticating both pseudonyms and trust levels to support trustworthy PSN with privacy preservation. The scheme achieves secure anonymous authentication with anonymity and conditional traceability on the basis of a trusted authority (TA). By applying a back-up solution, it can guarantee communications among nodes for an extended time period even when the TA is not available. In addition, the use of batch-signature verification further reduces the cost of authenticity verification of a large number of messages. Performance analysis and evaluation further prove that the proposed scheme is effective with regard to privacy preservation, computation complexity, communication cost, flexibility, reliability, and scalability.