Unreliable Users Research Articles

Feature Cluster-Based Secure Data Transmission Method for Social Internet of Vehicles

Due to the high mobility of vehicles and the high dynamics of SIoV network topology, the communication between users will be frequently interrupted, thus affecting the service quality of users. In addition, due to the open nature of the SIoV wireless channel, any user can broadcast messages in the system. However, unreliable users pose serious security threats to other users on the network. In order to solve these problems, we propose a feature cluster-based secure data transmission method (FC-SDTM) to ensure safe and stable data transmission between vehicles. This method creates feature clusters according to the feature similarity of users, which provides the stability of communication between users and improves users’ reliability in the cluster. Second, consortium blockchains store the transmission data sent by the sender in the cluster for the receiver to verify, further ensuring the security of intra-cluster communication. Finally, the random number key reduces the running time of the proposed method and solves the security problem caused by cluster topology updates. The experimental results demonstrate that this method can reduce the system running time and the message exposure rate, while also improving transmission accuracy.

Efficient Privacy-Preserving Federated Learning With Unreliable Users

Federated learning (FL) has emerged as a powerful technology widely applied in Internet of Things (IoT). Recently, researchers have shown an increased interest in privacy-preserving FL with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . The goal of such works is to achieve private training under ciphertext mode while ensuring that the FL model is mainly derived from the contributions of users with high-quality data. However, the existing work is still in its infancy, and the main challenge faced by many researchers is how to achieve their schemes for meeting the demands of high accuracy and efficiency. To combat that, we propose an efficient privacy-preserving FL (EPPFL) scheme with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . Specifically, we design a novel scheme to mitigate the negative impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> , where the targeted model is guaranteed to be updated with high-quality data. Through iteratively executing our “Excluding Irrelevant Components” and “Weighted Aggregation,” the FL model converges rapidly while taking limited communication and computation overhead. As a result, not only the model accuracy can be optimized, but also the training efficiency can be improved. Meanwhile, we conduct a secure framework based on the threshold Paillier cryptosystem, which can rigorously protect all user-related private information during the training process. Furthermore, the extensive experiments demonstrate our EPPFL with high-level performance in terms of accuracy and efficiency.

Analyzing the Effect of Exposure to COVID-19 Misinformation on Health Behaviors

American news media have become more diverse, siloed, and politically polarized in recent decades, leading Americans with increasingly varied news consumption patterns to receive disparate information. In addition, the accessibility of social media platforms to unreliable users has resulted in misinformation-sharing on many online platforms. This project aims to elucidate the origins of individuals’ COVID-19 pro- and anti-health behaviors — particularly in the context of their news and information consumption — by investigating relationships between self-reported exposure to true and false statements about COVID-19, belief in such statements, and COVID-19 pro- and anti-health behaviors. Furthermore, the effects of primary news sources, time spent on social media, and demographics on these variables were analyzed. COVID-19 statements were drawn from the C.D.C., W.H.O., and public polls by Pew Research Center. To collect data, a 27-question survey was distributed to 250 panelists from the general U.S. population. Relationships between pairs of variables were analyzed using chi-square tests, which showed that exposure to COVID-19 statements had a significant (P &lt; 0.05) effect on belief in statements, exposure to true statements had a significant effect on COVID-19 health behavior scores, and belief in statements had a significant effect on health behavior scores. In addition, time spent on social media, age, and political affiliation, had significant effects on these variables. This project adds to the growing body of research on the effects of information sources on health behaviors and sheds light on the genesis of COVID-19 pro- and anti-health behaviors.

A new model for trustworthy web service QoS prediction

AbstractThe number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

Incentive mechanisms are crucial for motivating adequate users to provide reliable data in mobile crowdsensing (MCS) systems. However, the privacy leakage of most existing incentive mechanisms leads to users unwilling to participate in sensing tasks. In this paper, we propose a privacy-preserving incentive mechanism based on truth discovery. Specifically, we use the secure truth discovery scheme to calculate ground truth and the weight of users’ data while protecting their privacy. Besides, to ensure the accuracy of the MCS results, a data eligibility assessment protocol is proposed to remove the sensing data of unreliable users before performing the truth discovery scheme. Finally, we distribute rewards to users based on their data quality. The analysis shows that our model can protect users’ privacy and prevent the malicious behavior of users and task publishers. In addition, the experimental results demonstrate that our model has high performance, reasonable reward distribution, and robustness to users dropping out.

Cognitive Radio Networks Based on Users' Reputation Scheme

Cognitive Radio (CR) can be defined as a technology that allows users to change the transmission parameters as required to increase the spectrum efficiency. Because of this mechanism, some threats emerge. Two major threats are found in CR. The first is the Primary User Emulation Attack (PUEA), where the attacker is able to transmit at a forbidden time slot effectively, emulating the signals of the primary user. This makes all the system users believe that the spectrum is occupied by a good primary user. The second threat is known as the spectrum sensing data falsification attack (SSDF). In this case, the attackers send false observation information, intentionally or unintentionally, to the fusion center (FC), causing it to make the wrong decision. In this study, the scheme presented was based on a users' reputation for secure spectrum access in cognitive radio networks. Each Secondary User (SU) performs local sensing and then forwards the sensing results to the main fusion center FC. The FC makes the final decision about the presence of the primary user based on the proposed approach. The schemes substantially reduce the effect of cognitive users with low reputation values while improving the impact of cognitive users with the high reputation values on the final decision. It has been verified that the proposed approach can improve the sensing performance under the impact of a different number of reliable and unreliable users in a CR network. Results based on simulation show that the proposed scheme outperforms the traditional majority scheme despite a high number of malicious users.

Web service quality of service prediction via regional reputation‐based matrix factorization

AbstractQuality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

A Review of Consensus Protocols in Permissioned Blockchains

Consensus protocols are used for the distributed management of large databases in an environment without trust among participants. The choice of a specific protocol depends on the purpose and characteristics of the system itself. The subjects of the paper are consensus protocols in permissioned blockchains. The objective of this paper is to identify functional advantages and disadvantages of observed protocol. The analysis covers a total of six consensus protocols for permissioned blockchains. The following characteristics were compared: security, trust among participants, throughput and scalability. The results show that no protocol shows absolute dominance in all aspects of the comparison. Paxos and Raft are intended for systems in which there is no suspicion of unreliable users, but only the problem of a temporary shutdown. Practical Byzantine Fault Tolerance is intended for systems with a small number of nodes. Federated Byzantine Fault Tolerance shows better scalability and is more suitable for large systems, but can withstand a smaller number of malicious nodes. Proof-of-authority can withstand the largest number of malicious nodes without interfering with the functioning of the system. When choosing a consensus protocol for a blockchain application, one should take into account priority characteristics.

Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection

Since the harmful consequences of the online publication of fake news have emerged clearly, many research groups worldwide have started to work on the design and creation of systems able to detect fake news and entities that share it consciously. Therefore, manifold automatic, manual, and hybrid solutions have been proposed by industry and academia. In this article, we describe a deep investigation of the features that both from an automatic and a human point of view, are more predictive for the identification of social network profiles accountable for spreading fake news in the online environment. To achieve this goal, the features of the monitored users were extracted from Twitter , such as social and personal information as well as interaction with content and other users. Subsequently, we performed (i) an offline analysis realized through the use of deep learning techniques and (ii) an online analysis that involved real users in the classification of reliable/unreliable user profiles. The experimental results, validated from a statistical point of view, show which information best enables machines and humans to detect malicious users. We hope that our research work will provide useful insights for realizing ever more effective tools to counter misinformation and those who spread it intentionally.

MeURep: A novel user reputation calculation approach in personalized cloud services.

User reliability is notably crucial for personalized cloud services. In cloud computing environments, large amounts of cloud services are provided for users. With the exponential increase in number of cloud services, it is difficult for users to select the appropriate services from equivalent or similar candidate services. The quality-of-service (QoS) has become an important criterion for selection, and the users can conduct personalized selection according to the observed QoS data of other users; however, it is difficult to ensure that the users are reliable. Actually, unreliable users may provide unreliable QoS data and have negative effects on the personalized cloud service selection. Therefore, how to determine reliable QoS data for personalized cloud service selection remains a significant problem. To measure the reliability for each user, we present a cloud service selection framework based on user reputation and propose a new user reputation calculation approach, which is named MeURep and includes L1-MeURep and L2-MeURep. Experiments are conducted, and the results confirm that MeURep has higher efficiency than previously proposed approaches.

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization.

Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks

With the development of online applications based on social networks, many different approaches have emerged to evaluate the service that these applications provide. Reports made by end users regarding the consumer's experience or opinion are commonly used to rate the quality of different online services. Therefore, ensuring the authenticity of the users' reports, and the detection of malicious users' dishonest reports, have both become important issues to achieve accuracy in the rating of such services. In this paper, we propose and evaluate a private-prior peer prediction-based trustworthy service rating system, which requires users to report their prior and posterior beliefs regarding whether their peers will report a high-quality opinion of the service. The reports are made to a data processing center which evaluates the users' trustworthiness by applying a strictly proper scoring rule, and removes reports received from users whose trustworthiness rating is low. This peer prediction method is compatible with incentives to motivate users to report honestly. In addition, an unreliability index is proposed to identify malicious users, and malfunctioning or unreliable users who have a high error rate in making judgments about quality. Thus, reports with high unreliability values will also be excluded from the service rating system. By combining trustworthiness and unreliability, malicious users face the dilemma that they cannot receive both a high trustworthiness and low unreliability rating simultaneously when their reports are false. Simulation results indicate that the proposed peer prediction-based trustworthy service rating can identify malicious and unreliable behaviors effectively and motivate users to report truthfully, and that a relatively high service rating accuracy is achieved by the proposed system.

A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

With the growing popularity of cloud manufacturing (CMfg), an increasing number of functionally equivalent cloud services are available on the Internet, which makes cloud service recommendation an important topic recently. Since the quality-of-service (QoS) varies largely in cloud environment, many Collaborative Filtering (CF) approaches are recently proposed to predict the uncertain QoS by utilizing the historical QoS records of similar users. In addition to the data sparsity problem, however, most existing approaches also ignore the task similarity among different users, while dissimilar tasks requested by similar users may lead to quite different QoS prediction results, and thus cannot achieve higher prediction accuracy and recommendation quality. To address this problem, we develop a novel clustering-based and trust-aware approach. Firstly, a set of similar users can be identified from the view of task similarity using clustering-based algorithm, where task similarity can be computed by incorporating both explicit textual information and rating information as well as implicit context information. Secondly, considering the fact that the QoS values may be contributed by unreliable users, we design a trust-aware CF approach by merging local and global trust values, to reconstruct trust network of the clustered users. Finally, we combine the clustering-based algorithm and trust-aware approach to make a more personalized QoS prediction and reliable cloud service recommendation for the active user in CMfg. Experimental results on two real-world data sets demonstrate the effectiveness of the proposed approach compared with other state-of-the-art approaches.

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments

Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service varies according to different users' perspectives, the users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar users. However, users with a low reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation; and therefore, how to calculate a personalized reputation for each user at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online reputation calculation method, called the OPRC, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable users and 15 unreliable users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.

Eliminating the Effect of Rating Bias on Reputation Systems

The ongoing rapid development of the e‐commercial and interest‐base websites makes it more pressing to evaluate objects’ accurate quality before recommendation. The objects’ quality is often calculated based on their historical information, such as selected records or rating scores. Usually high quality products obtain higher average ratings than low quality products regardless of rating biases or errors. However, many empirical cases demonstrate that consumers may be misled by rating scores added by unreliable users or deliberate tampering. In this case, users’ reputation, that is, the ability to rate trustily and precisely, makes a big difference during the evaluation process. Thus, one of the main challenges in designing reputation systems is eliminating the effects of users’ rating bias. To give an objective evaluation of each user’s reputation and uncover an object’s intrinsic quality, we propose an iterative balance (IB) method to correct users’ rating biases. Experiments on two datasets show that the IB method is a highly self‐consistent and robust algorithm and it can accurately quantify movies’ actual quality and users’ stability of rating. Compared with existing methods, the IB method has higher ability to find the “dark horses,” that is, not so popular yet good movies, in the Academy Awards.

Trust-Oriented Partner Selection in D2D Cooperative Communications

To meet the rapid growing requirement of data services and offload the heavy base station load, device-to-device (D2D) communication is proposed as one of the key technologies for future cellular network, which can enable users to communicate directly and thus effectively improve the bearing capacity of the network. However, when D2D users transmit packets in the manner of cooperative communication, the cooperative users with non-cooperative behavior, such as the selfishness of user, may lead to a sharp decline in network performance. For this reason, in our paper, a trust-oriented partner selection mechanism (TPSM) is proposed to avoid choosing those users with non-cooperative behavior. In particular, the related psychological researches show that the psychology structure of users can be divided into three aspects: cognition, emotion, and behavior. Considering the psychology structure of users, we build multi-dimensional trust relationships between sending users and cooperative users by evaluating the cognition trust, emotion trust, and behavior trust. Furthermore, aiming at the multi-dimensional trust relationships between sending users and cooperative users, we classify the cooperative users into three categories, namely reliable users, observed users, and unreliable users, by combining the decision-theoretic rough sets based on Naive Bayes. Finally, based on different cooperative transmission scenarios, an optimal partner selection mechanism is proposed in this paper. The optimal partner can be chosen from the reliable cooperative users, according to the different scenarios and factors. The numerical results show that the proposed TPSM can effectively identify the selfish users and notably enhance the packet delivery rate.

Web Service Personalized Quality of Service Prediction via Reputation-Based Matrix Factorization

With the fast development of Web services in service-oriented systems, the requirement of efficient Quality of Service (QoS) evaluation methods becomes strong. However, many QoS values are unknown in reality. Therefore, it is necessary to predict the unknown QoS values of Web services based on the obtainable QoS values. Generally, the QoS values of similar users are employed to make predictions for the current user. However, the QoS values may be contributed from unreliable users, leading to inaccuracy of the prediction results. To address this problem, we present a highly credible approach, called reputation-based Matrix Factorization (RMF), for predicting the unknown Web service QoS values. RMF first calculates the reputation of each user based on their contributed QoS values to quantify the credibility of users, and then takes the users' reputation into consideration for achieving more accurate QoS prediction. Reputation-based matrix factorization is applicable to the prediction of QoS data in the presence of unreliable user-provided QoS values. Extensive experiments are conducted with real-world Web service QoS data sets, and the experimental results show that our proposed approach outperforms other existing approaches.

Secure Cooperative Spectrum Sensing via a Novel User-Classification Scheme in Cognitive Radios for Future Communication Technologies

Future communication networks would be required to deliver data on a far greater scale than is known to us today, thus mandating the maximal utilization of the available radio spectrum using cognitive radios. In this paper, we have proposed a novel cooperative spectrum sensing approach for cognitive radios. In cooperative spectrum sensing, the fusion center relies on reports of the cognitive users to make a global decision. The global decision is obtained by assigning weights to the reports received from cognitive users. Computation of such weights requires prior information of the probability of detection and the probability of false alarms, which are not readily available in real scenarios. Further, the cognitive users are divided into reliable and unreliable categories based on their weighted energy by using some empirical threshold. In this paper, we propose a method to classify the cognitive users into reliable, neutral and unreliable categories without using any pre-defined or empirically-obtained threshold. Moreover, the computation of weights does not require the detection, or false alarm probabilities, or an estimate of these probabilities. Reliable cognitive users are assigned the highest weights; neutral cognitive users are assigned medium weights (less than the reliable and higher than the unreliable cognitive users’ weights); and unreliable users are assigned the least weights. We show the performance improvement of our proposed method through simulations by comparing it with the conventional cooperative spectrum sensing scheme through different metrics, like receiver operating characteristic (ROC) curve and mean square error. For clarity, we also show the effect of malicious users on detection probability and false alarm probability individually through simulations.

Ranking Reputation and Quality in Online Rating Systems

How to design an accurate and robust ranking algorithm is a fundamental problem with wide applications in many real systems. It is especially significant in online rating systems due to the existence of some spammers. In the literature, many well-performed iterative ranking methods have been proposed. These methods can effectively recognize the unreliable users and reduce their weight in judging the quality of objects, and finally lead to a more accurate evaluation of the online products. In this paper, we design an iterative ranking method with high performance in both accuracy and robustness. More specifically, a reputation redistribution process is introduced to enhance the influence of highly reputed users and two penalty factors enable the algorithm resistance to malicious behaviors. Validation of our method is performed in both artificial and real user-object bipartite networks.