Foundation models have demonstrated remarkable capabilities in handling diverse modalities and tasks, outperforming conventional artificial intelligence (AI) approaches that are highly task-specific and modality-reliant.In the medical domain, however, the development of comprehensive foundation models is constrained by limited access to diverse modalities and stringent privacy regulations.To address these constraints, this study introduces a novel knowledge injection approach, FED-KIM, designed to scale the medical foundation model within a federated learning framework.FEDKIM leverages lightweight local models to extract healthcare knowledge from private data and integrates this knowledge into a centralized foundation model using a designed adaptive Multitask Multimodal Mixture Of Experts (M 3 OE) module.This method not only preserves privacy but also enhances the model's ability to handle complex medical tasks involving multiple modalities.Our extensive experiments across twelve tasks in seven modalities demonstrate the effectiveness of FEDKIM in various settings, highlighting its potential to scale medical foundation models without direct access to sensitive data.

Health risk prediction aims to forecast the potential health risks that patients may face using their historical Electronic Health Records (EHR). Although several effective models have developed, data insufficiency is a key issue undermining their effectiveness. Various data generation and augmentation methods have been introduced to mitigate this issue by expanding the size of the training data set through learning underlying data distributions. However, the performance of these methods is often limited due to their task-unrelated design. To address these shortcomings, this paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. Furthermore, MedDiffusion discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data. Experimental evaluation on four real-world medical datasets demonstrates that MedDiffusion outperforms 14 cutting-edge baselines in terms of PR-AUC, F1, and Cohen's Kappa. We also conduct ablation studies and benchmark our model against GAN-based alternatives to further validate the rationality and adaptability of our model design. Additionally, we analyze generated data to offer fresh insights into the model's interpretability. The source code is available via https://shorturl.at/aerT0.

Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the Internet of Things (IoT). However, end IoT devices do not have abilities to automatically annotate their collected data, which leads to the label shortage issue at the client side. To collaboratively train an FL model, we can only use a small number of labeled data stored on the server. This is a new yet practical scenario in federated learning, i.e., labels-at-server semi-supervised federated learning (SemiFL). Although several SemiFL approaches have been proposed recently, none of them can focus on the personalization issue in their model design. IoT environments make SemiFL more challenging, as we need to take device computational constraints and communication cost into consideration simultaneously. To tackle these new challenges together, we propose a novel SemiFL framework named pFedKnow. pFedKnow generates lightweight personalized client models via neural network pruning techniques to reduce communication cost. Moreover, it incorporates pretrained large models as prior knowledge to guide the aggregation of personalized client models and further enhance the framework performance. Experiment results on both image and text datasets show that the proposed pFedKnow outperforms state-of-the-art baselines as well as reducing considerable communication cost. The source code of the proposed pFedKnow is available at https://github.com/JackqqWang/pfedknow/tree/master.

Truth discovery algorithms have been widely applied to identify the true claims from the conflicting information provided by multiple sources. In general, they conduct an iterative procedure to estimate source reliability degrees as weights and infer the true claims via weighted voting. However, there is little prior work that provides theoretical analysis on the convergence of truth discovery methods. In this paper, we formulated the truth discovery task as a joint maximum likelihood estimation (JMLE) problem for unknown source reliability and truth claims. Within this framework, we proposed a Unified Truth Discovery (UTD) algorithm to get the numerical solution to JMLE for truth and source reliability. With mild conditions, we proved the consistency of the JMLE and the convergence of the proposed UTD algorithm. In addition, our proposed UTD algorithm turns out to include many existing truth discovery algorithms as special cases. This guarantees that our theoretical results can be applied to these truth discovery algorithms. We further conduct extensive experiments on synthetic data sets as well as five real-world data sets, and results from these numerical analysis support the theoretical results of the proposed UTD algorithm and the other state-of-the-art truth discovery algorithms.

Researchers have conduct adversarial attacks against deep neural networks (DNNs) for health risk prediction in the white/gray-box setting to evaluate their robustness. However, since most real-world solutions are trained by private data and released as black-box services on the cloud, we should investigate their robustness in the black-box setting. Unfortunately, existing work ignores to consider the uniqueness of electronic health records (EHRs). To fill this gap, we propose the first black-box adversarial attack method against health risk prediction models named MedAttacker to investigate their vulnerability. It addresses the challenges brought by EHRs via two steps: hierarchical position selection which selects the attacked positions in a reinforcement learning (RL) framework and substitute selection which identifies substitutes with a score-based principle. Particularly, by considering the temporal context inside EHRs, MedAttacker initializes its RL position selection policy by using the contribution score of each visit and the saliency score of each code, which can be well integrated with the deterministic substitute selection process decided by the score changes. We evaluate MedAttacker by attacking three advanced risk prediction models in the black-box setting across multiple real-world datasets, and MedAttacker consistently achieves the highest average success rate and even outperforms a recent white-box EHR adversarial attack technique in certain cases.

In the era of big data, data are usually distributed across numerous connected computing and storage units (i.e., nodes or workers). Under such an environment, many machine learning problems can be reformulated as a consensus optimization problem, which consists of one objective and constraint terms splitting into N parts (each corresponds to a node). Such a problem can be solved efficiently in a distributed manner via Alternating Direction Method of Multipliers ( ADMM ). However, existing consensus optimization frameworks assume that every node has the same quality of information (QoI) , i.e., the data from all the nodes are equally informative for the estimation of global model parameters. As a consequence, they may lead to inaccurate estimates in the presence of nodes with low QoI. To overcome this challenge, in this article, we propose a novel consensus optimization framework for distributed machine-learning that incorporates the crucial metric, QoI. Theoretically, we prove that the convergence rate of the proposed framework is linear to the number of iterations, but has a tighter upper bound compared with ADMM . Experimentally, we show that the proposed framework is more efficient and effective than existing ADMM -based solutions on both synthetic and real-world datasets due to its faster convergence rate and higher accuracy.

Federated Learning has shown great potentials for the distributed data utilization and privacy protection. Most existing federated learning approaches focus on the supervised setting, which means all the data stored in each client has labels. However, in real-world applications, the client data are impossible to be fully labeled. Thus, how to exploit the unlabeled data should be a new challenge for federated learning. Although a few studies are attempting to overcome this challenge, they may suffer from information leakage or misleading information usage problems. To tackle these issues, in this paper, we propose a novel federated semi-supervised learning method named FedTriNet, which consists of two learning phases. In the first phase, we pre-train FedTriNet using labeled data with FedAvg. In the second phase, we aim to make most of the unlabeled data to help model learning. In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set. Finally, FedTriNet uses the new training set to retrain the model. Experimental results on three publicly available datasets show that the proposed FedTriNet outperforms state-of-the-art baselines under both IID and Non-IID settings.

Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significantly among different users. To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic. Although the existing truth discovery approaches can provide reliable aggregated results, they fail to protect the private information of individual users. Moreover, crowd sensing systems typically involve a large number of participants, making encryption or secure multi-party computation based solutions difficult to deploy. To address these challenges, in this paper, we propose an efficient privacy-preserving truth discovery mechanism with theoretical guarantees of both utility and privacy. The key idea of the proposed mechanism is to perturb data from each user independently and then conduct weighted aggregation among users’ perturbed data. The proposed approach is able to assign user weights based on information quality, and thus the aggregated results will not deviate much from the true results even when large noise is added. We adapt local differential privacy definition to this privacy-preserving task and demonstrate the proposed mechanism can satisfy local differential privacy while preserving high aggregation accuracy. We formally quantify utility and privacy trade-off and further verify the claim by experiments on both synthetic data and a real-world crowd sensing system.

Predicting diseases for patients is an important and practical task in healthcare informatics. Existing disease prediction models focus on common diseases, i.e., there are enough available EHR data and prior medical knowledge for analyzing them. However, those models may not work for rare disease prediction as it is extremely hard to collect enough EHR data with such diseases. To tackle these issues, in this paper, we design a novel rare disease prediction system, which not only generates EHR data but also automatically selects high-quality generated data to further improve the predictive performance. Three components are designed in the system: data generation, data selection, and prediction. In particular, we propose MaskEHR to generate diverse EHR data based on the data from patients suffering from the given diseases. To remove noise information in the generated EHR data, we further design a reinforcement learning-based data selector, called RL-Selector, which can automatically choose the high-quality generated EHR data. Finally, the prediction component is used to identify patients who will potentially suffer the given diseases. These three components work together and enhance each other. Experiments on three real healthcare datasets show that the proposed system outperforms existing approaches on rare disease prediction task.

Crowdsourcing has become a popular way to obtain a large volume of labeled data due to its low cost and high efficiency. Usually the crowdsourcing process enables redundancy in the collected labels in order to ensure the correctness of item labels. However, workers on the crowdsourcing platform may make mistakes on some items, leading to inconsistent labels. In this case, it is important to aggregate these noisy labels and obtain the true labels of the items. The correctness of the item label provided by a worker depends on both the worker's ability and the property of the item. However, most of the existing models consider the effect of workers' abilities but ignore that of the item properties. In this paper, we propose a novel crowdsourcing aggregation method (IProWA) which incorporates the modeling of not only worker expertise level but also item property. In particular, items are represented by a K dimensional vector (i.e., item parameter), where K is the number of possible categories and each dimension represents a category. The proposed model transforms the true label estimation into the estimation of item parameters as it connects the true label and the parameters of an item. In worker modeling, it models the different category propensities among different worker groups. Experimental results show that the performance of the proposed model is comparable to that of the state-of-the-art baselines and the learned item parameters can help interpret the property of that item.

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect not only users’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users’ encrypted data using a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.

The goal of diagnosis prediction task is to predict the future health information of patients from their historical Electronic Healthcare Records (EHR). The most important and challenging problem of diagnosis prediction is to design an accurate, robust and interpretable predictive model. Existing work solves this problem by employing recurrent neural networks (RNNs) with attention mechanisms, but these approaches suffer from the data sufficiency problem. To obtain good performance with insufficient data, graph-based attention models are proposed. However, when the training data are sufficient, they do not offer any improvement in performance compared with ordinary attention-based models. To address these issues, we propose KAME, an end-to-end, accurate and robust model for predicting patients' future health information. KAME not only learns reasonable embeddings for nodes in the knowledge graph, but also exploits general knowledge to improve the prediction accuracy with the proposed knowledge attention mechanism. With the learned attention weights, KAME allows us to interpret the importance of each piece of knowledge in the graph. Experimental results on three real world datasets show that the proposed KAME significantly improves the prediction performance compared with the state-of-the-art approaches, guarantees the robustness with both sufficient and insufficient data, and learns interpretable disease representations.

Design flows are the explicit combinations of design transformations, primarily involved in synthesis, placement and routing processes, to accomplish the design of Integrated Circuits (ICs) and System-on-Chip (SoC). Mostly, the flows are developed based on the knowledge of the experts. However, due to the large search space of design flows and the increasing design complexity, developing Intellectual Property (IP)-specific synthesis flows providing high Quality of Result (QoR) is extremely challenging. This work presents a fully autonomous framework that artificially produces design-specific synthesis flows without human guidance and baseline flows, using Convolutional Neural Network (CNN). The demonstrations are made by successfully designing logic synthesis flows of three large scaled designs.

A tensor (i.e., an N-mode array) is a natural representation for multidimensional data. Tucker Decomposition (TD) is one of the most popular methods, and a series of batch TD algorithms have been extensively studied and widely applied in signal/image processing, bioinformatics, etc. However, in many applications, the large-scale tensor is dynamically evolving at all modes, which poses significant challenges for existing approaches to track the TD for such dynamic tensors. In this paper, we propose an efficient Online Tucker Decomposition (eOTD) approach to track the TD of dynamic tensors with an arbitrary number of modes. We first propose corollaries on the multiplication of block tensor matrix. Based on this corollary, eOTD allows us 1) to update the projection matrices using those projection matrices from the previous timestamp and the auxiliary matrices from the current timestamp, and 2) to update the core tensor by a sum of tensors that are obtained by multiplying smaller tensors with matrices. The auxiliary matrices are obtained by solving a series of least square regression tasks, not by performing Singular Value Decompositions (SVD). This overcomes the bottleneck in computation and storage caused by computing SVDs on largescale data. A Modified Gram-Schmidt (MGS) process is further applied to orthonormalize the projection matrices. Theoretically, the output of the eOTD framework is guaranteed to be lowrank. We further prove that the MGS process will not increase Tucker decomposition error. Empirically, we demonstrate that the proposed eOTD achieves comparable accuracy with a significant speedup on both synthetic and real data, where the speedup can be more than 1,500 times on large-scale data.

With the proliferation of sensor-rich mobile devices, crowd sensing has emerged as a new paradigm of collecting information from the physical world. However, the sensory data provided by the participating workers are usually not reliable. In order to identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate each worker's reliability and infer the underlying truths through weighted data aggregation, is widely studied. Since truth discovery incorporates workers' reliability into the aggregation procedure, it shows robustness to the data poisoning attacks, which are usually conducted by the malicious workers who aim to degrade the effectiveness of the crowd sensing systems through providing malicious sensory data. However, truth discovery is not perfect in all cases. In this paper, we study how to effectively conduct two types of data poisoning attacks, i.e., the availability attack and the target attack, against a crowd sensing system empowered with the truth discovery mechanism. We develop an optimal attack framework in which the attacker can not only maximize his attack utility but also disguise the introduced malicious workers as normal ones such that they cannot be detected easily. The desirable performance of the proposed framework is verified through extensive experiments conducted on a real-world crowd sensing system.

Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.

Diabetes is a serious disease affecting a large number of people. Although there is no cure for diabetes, it can be managed. Especially, with advances in sensor technology, lots of data may lead to the improvement of diabetes management, if properly mined. However, there usually exists noise or errors in the observed behavioral data which poses challenges in extracting meaningful knowledge. To overcome this challenge, we learn the latent state which represents the patient's condition. Such states should be inferred from the behavioral data but unknown a priori. In this paper, we propose a novel framework to capture the trajectory of latent states for patients from behavioral data while exploiting their demographic differences and similarities to other patients. We conduct a hypothesis test to illustrate the importance of the demographic data in diabetes management, and validate that each behavioral feature follows an exponential or a Gaussian distribution. Integrating these aspects, we use a Demographic feature restricted hidden Markov model (DfrHMM) to estimate the trajectory of latent states by integrating the demographic and behavioral data. In DfrHMM, the latent state is mainly determined by the previous state and the demographic features in a nonlinear way. Markov Chain Monte Carlo techniques are used for model parameter estimation. Experiments on synthetic and real datasets show that DfrHMM is effective in diabetes management.

The detection of malicious behavior, that is, judging if a host/domain is malicious or benign (i.e., negative or positive labels), is complicated by the issue of imbalanced label distributions, as well as the limited amount of ground truth available to train supervised models or build rules. To tackle these challenges, we propose a novel framework to learn cost-sensitive models on both network hosts and external domains simultaneously, based on a bipartite connectivity graph constructed between them. We also explicitly incorporate behavioral features of the hosts computed from the network data as well as lexical and reputational features computed for the external domains into the proposed framework. Specifically, we model the predicted labels, measure the misclassification errors by the Hamming distance between the predicted and true labels, incorporate different costs for different misclassification types (i.e., false negative or false positive), and constrain connected nodes to share the same labels in high probability. The proposed framework is then formulated as an optimization problem, which minimizes the total cost, that is, the misclassification costs multiplied by the misclassification errors. As the Hamming distance function is non-differentiable, we introduce a continuous loss function to approximate it with performance guaranteed. We develop an effective algorithm with good convergence property via Stochastic Gradient Descent technique. Experimental results on both synthetic and a real network dataset collected from an enterprise demonstrate the effectiveness of the proposed framework.

In the age of big data, information for the same entity can be obtained from different sources, which is inevitably conflicting. Therefore, aggregation methods are needed to identify the trustworthy information from such conflicting data. Truth discovery, which improves the aggregation results by estimating source trustworthiness and discovering truths simultaneously, has become an emerging field. Most truth discovery methods assume that sources make their claims independently, which may not be true in practice. As a matter of fact, influences among sources are ubiquitous and the claims made by one source may be influenced by others. Although there is some work that considers source correlation, those methods are designed to handle categorical claims, which is not general enough to represent the complicated real world applications. To tackle these challenges in truth discovery, we propose an unsupervised probabilistic model named IATD. The model takes source correlations as prior for influence derivation. To model influences among sources, we introduce "claim trustworthiness", which fuses the trustworthiness of the source which provides the claim and the trustworthiness of its influencers. Besides, the proposed model can handle different data types using different distributions in the probabilistic model. Experiments on real-world datasets show that IATD model can improve the aggregation performance compared with the state-of-the-art truth discovery approaches. The properties of IATD model are further illustrated using simulated datasets.

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.

In the information age, people can easily collect information about the same set of entities from multiple sources, among which conflicts are inevitable. This leads to an important task, truth discovery, i.e., to identify true facts (truths) via iteratively updating truths and source reliability. However, the convergence to the truths is never discussed in existing work, and thus there is no theoretical guarantee in the results of these truth discovery approaches. In contrast, in this paper we propose a truth discovery approach with theoretical guarantee. We propose a randomized gaussian mixture model (RGMM) to represent multi-source data, where truths are model parameters. We incorporate source bias which captures its reliability degree into RGMM formulation. The truth discovery task is then modeled as seeking the maximum likelihood estimate (MLE) of the truths. Based on expectation-maximization (EM) techniques, we propose population-based (i.e., on the limit of infinite data) and sample-based (i.e., on a finite set of samples) solutions for the MLE. Theoretically, we prove that both solutions are contractive to an ε-ball around the MLE, under certain conditions. Experimentally, we evaluate our method on both simulated and real-world datasets. Experimental results show that our method achieves high accuracy in identifying truths with convergence guarantee.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from past literature, we capture such interactive effect, and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data, and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis, as well as extensive simulations.

Driven by the proliferation of sensor-rich mobile devices, crowd sensing has emerged as a new paradigm of gathering information about the physical world. In crowd sensing applications, user observations are usually unevenly distributed across the monitored entities, and this gives rise to two major challenges -- redundancy and sparsity. On one hand, multiple users may observe the same entity, and their observations are sometimes conflicting with each other due to the unreliable nature of human-carried sensors. On the other hand, crowd sensing data are usually very sparse, and there may exist considerable number of entities that never receive any observations from users. Some existing work studies these two challenges separately. However, we can gain great benefits by dealing with them jointly. In this paper, we develop an integrated framework to estimate the true values of entities from redundant and sparse data in crowd sensing applications. In this framework, we propose an effective algorithm to infer the "missing" observations for each entity, and aggregate both user-contributed and inferred observations to discover the true values of entities. We conduct extensive experiments on real-world crowd sensing systems to demonstrate the advantages of the proposed framework on correctly inferring entity truths from redundant and sparse data.

Sequential data modeling has received growing interests due to its impact on real world problems. Sequential data is ubiquitous - financial transactions, advertise conversions and disease evolution are examples of sequential data. A long-standing challenge in sequential data modeling is how to capture the strong hidden correlations among complex features in high volumes. The sparsity and skewness in the features extracted from sequential data also add to the complexity of the problem. In this paper, we address these challenges from both discriminative and generative perspectives, and propose novel stochastic learning algorithms to model nonlinear variances from static time frames and their transitions. The proposed model, Deep Recurrent Network (DRN), can be trained in an unsupervised fashion to capture transitions, or in a discriminative fashion to conduct sequential labeling. We analyze the conditional independence of each functional module and tackle the diminishing gradient problem by developing a two-pass training algorithm. Extensive experiments on both simulated and real-world dynamic networks show that the trained DRN outperforms all baselines in the sequential classification task and obtains excellent performance in the regression task.

A vast ocean of data is collected every day, and numerous applications call for the extraction of actionable insights from data. One important task is to detect untrustworthy information because such information usually indicates critical, unusual, or suspicious activities. In this paper, we study the important problem of detecting untrustworthy information from a novel perspective of correlating and comparing multiple sources that describe the same set of items. Different from existing work, we recognize the importance of time dimension in modeling the commonalities among multiple sources. We represent dynamic multi-source data as tensors and develop a joint non-negative tensor factorization approach to capture the common patterns across sources. We then conduct a comparison between source input and common patterns to identify inconsistencies as an indicator of untrustworthiness. An incremental factorization approach is developed to improve the computational efficiency on dynamically arriving data. We also propose a method to handle data sparseness. Experiments are conducted on hotel rating, network traffic flow, and weather forecast data that are collected from multiple sources. Results demonstrate the advantages of the proposed approach in detecting inconsistent and untrustworthy information.

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate user quality and infer truths through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to take into consideration an important issue in their design, i.e., the protection of individual users' private information. In this paper, we propose a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users' encrypted data using homomorphic cryptosystem. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Through extensive experiments on not only synthetic data but also real world crowd sensing systems, we justify the guarantee of strong privacy and high accuracy of our proposed framework.