The heavy use of social media platforms is generating a high volume of affective data over the internet. This data is being used by researchers in various domains for prediction, qualitative, and quantitative analytical problems such as stock market prediction, opinion mining of online reviews on products, events, and many more. This article leverages X data for the political response analysis of users towards the 2019 Indian General election. In this article, a methodology is proposed that analyses X data to know what topics were mostly discussed during the election time under the #LoksabhaElection2019 hashtag. Also, we have tried to find out the sentiments of people towards different political terms (words) in the topics inferred. For this task, the study has used topic modeling and sentiment analysis of Tweets. This research may be useful for political parties or newsgroups to mine main topics and analyze the sentiments of people towards different entities.

Digital Twin (DT) technology has gained significant attention for simulating and optimizing urban traffic systems, especially in intersection vehicle trajectory prediction. However, digital twin traffic system faces significant challenges due to privacy and security regulations that prevent the centralized storage of trajectory and semantic data, which are essential for training accurate predictive models using sensitive traffic information. To address these issues, we introduce the integration of federated learning spatio-temporal-semantic attention-based trajectory (FedSTAST) model into the DT framework for vehicle trajectory prediction. In our FedSTAST, edge servers in physical space utilize local sensor data to perform computations and train models without transmitting raw data, only the model parameters are sent to a cloud server in the twin space for aggregation. This decentralized approach ensures data privacy while enabling collaborative model training. The simulation results demonstrate that the FedSTAST model effectively handles co-training and multi-source semantic input processing within spatio-temporal-semantic attention-based trajectory (STSAT) models, enhancing trajectory prediction accuracy and robustness in the dynamic, real-time context of DT-based urban traffic systems.

With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework has shown promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges, such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous FL approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.

The rapid growth of social media platforms has led to an unprecedented increase in user-generated content and social interactions, posing significant challenges for recommendation systems. This article addresses the challenges of recommendation in large-scale dynamic social environments, where user interactions and preferences evolve rapidly across vast networks. In large-scale dynamic social networks, knowledge discovery requires methods to efficiently process vast amounts of data while capturing the evolving nature of user interactions and preferences. Multigraph neural networks offer a promising approach for this task, as they can model complex relationships and temporal dynamics in these environments. This article proposes a novel social-aware multigraph neural network for the session-based recommendation (SAMGNN-SR) model that leverages dynamic social information and multigraph neural networks to enhance recommendation accuracy and knowledge discovery in complex social computing environments. The model constructs a global social-aware interaction graph from all user session sequences and employs an adaptive subgraph sampling strategy to extract relevant collaborative signals efficiently. A dynamic interest extraction module utilizing dual-direction information propagation captures users' evolving preferences, while a social information fusion network based on graph attention mechanisms models the dynamic nature of social influences. Experiments on three real-world datasets (Douban, Delicious, and Yelp) demonstrate the superiority of SAMGNN-SR over nine state-of-the-art baselines, with improvements of up to 6.79% in NDCG@20 and 6.19% in Hit@20. Ablation studies validate the effectiveness of each model component in capturing complex social dynamics and session-based user behaviors.

Abstract In a smart city, IoT devices are required to support monitoring of normal operations such as traffic, infrastructure, and the crowd of people. IoT‐enabled systems offered by many IoT devices are expected to achieve sustainable developments from the information collected by the smart city. Indeed, artificial intelligence (AI) and machine learning (ML) are well‐known methods for achieving this goal as long as the system framework and problem statement are well prepared. However, to better use AI/ML, the training data should be as global as possible, which can prevent the model from working only on local data. Such data can be obtained from different sources, but this induces the privacy issue where at least one party collects all data in the plain. The main focus of this article is on support vector machines (SVM). We aim to present a solution to the privacy issue and provide confidentiality to protect the data. We build a privacy‐preserving scheme for SVM (SecretSVM) based on the framework of federated learning and distributed consensus. In this scheme, data providers self‐organize and obtain training parameters of SVM without revealing their own models. Finally, experiments with real data analysis show the feasibility of potential applications in smart cities. This article is the extended version of that of Hsu et al. (Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ACM; 2020:904‐906).

The emergence of edge computing guarantees the combination of the Internet of Things (IoT) and artificial intelligence (AI). The vertical federated learning (VFL) framework, usually deployed by split learning, can analyze and integrate information on different features collected by different terminals in the IoT. The complete model is divided into a top model and multiple bottom models in a specific middle layer. Each passive party as a terminal with certain features owns a bottom model, and an active party as an edge server with labels holds the top model. Feature inference attack aims to infer the party’s features from the model predictions during prediction in VFL. Existing attacks considered the adversary an active party under the white-box or black-box model. However, an attacker usually is a passive party in practice because terminals are more vulnerable than edge servers. Therefore, this article discusses a practical feature inference attack in VFL during prediction in IoT under this setting. We design an adversary builds an inference model to minimize the distance between the predictions from the inferred features and target features. Because the information on the top model and other bottom models is unknown, the adversary cannot directly train the inference model. Therefore, we utilize the zeroth-order gradient estimation method to calculate the parameters’ gradients to train the inference model. Experimental results demonstrate that the performance of our attack is comparable to that of the white-box attacks while retaining apparent advantages over the existing black-box attacks.

Summary Recently, an authentication scheme was presented for health‐care in IoT for WBANs by Fotouhi et al. Authors asserted their scheme to be free from a number of attacks. Here, we find that Fotouhi et al.'s scheme suffers from insider attack, fails to provide proper authentication and bears inefficient password update phase. Therefore their scheme has security pitfalls that can lead to further security breaches. We remove the weaknesses of Fotouhi et al.'s scheme and hence propose a user authentication scheme for patient care. Our scheme is suitable for providing special care to patients in the healthcare industry. The proposed scheme facilitates health professionals to access the real‐time data of patients under treatment for some diseases. It also safeguards the medical staff from catching an infection from the patients by minimizing the need to come in direct contact with the patients. We justify the security of our proposed scheme by applying the random oracle model to it. The proposed scheme is compared with some related works to judge its efficacy over the compared counterparts.

In recent years, voice-interaction-based control systems have attracted considerable attention for industrial control systems implementing Industrial Internet of Things (IIoT) technologies. The development of automated semantic understanding relates to the industrial Internet equipment used to realize remote voice control as well as to its intelligent management and control. In these emerging voice-interaction-enabled industrial central control systems, sorting technologies are considered critical. For complex user questions, the level of satisfaction regarding the answers given by such systems tends to be low. Driven by these challenges and opportunities, the optimization of conventional retrieval-based question answering through deep learning methods has become popular. In this study, we propose three deep semantic sorting models based on deep learning, including a multilayer convolutional matching sorting model for single documents and two interactive pairwise bidirectional encoder representations from transformers (BERT) sorting models for document pairs. Two main network architectures are proposed to model document pairs, named Pairwise-Twin-BERT and Pairwise-Triple-BERT. Experimental results indicate that proposed models performed better than state-of-the-art methods based on text matching in a candidate document sorting task.

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT) and machine learning technologies have been widely used to achieve the intelligent control and routing planning for ships. As an important branch of machine learning, federated learning is the first choice to train an accurate joint model without sharing ships' data directly. However, there are still many unsolved challenges while using federated learning in IoT-enabled MTS, such as the privacy preservation and Byzantine attacks. To surmount the above challenges, a novel mechanism, namely DisBezant, is designed to achieve the secure and Byzantine-robust federated learning in IoT-enabled MTS. Specifically, a credibility-based mechanism is proposed to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships. The credibility is introduced to measure the trustworthiness of uploaded knowledge from ships and is updated based on their shared information in each epoch. Then, we design an efficient privacy-preserving gradient aggregation protocol based on a secure two-party calculation protocol. With the help of a central server, we can accurately recognise the Byzantine attackers and update the global model parameters privately. Furthermore, we theoretically discussed the privacy preservation and efficiency of DisBezant. To verify the effectiveness of our DisBezant, we evaluate it over three real datasets and the results demonstrate that DisBezant can efficiently and effectively achieve the Byzantine-robust federated learning. Although there are 40% nodes are Byzantine attackers in participants, our DisBezant can still recognise them and ensure the accurate model training.

Contrary to the public cloud storage services that impose users to accept the security restrictions delivered by the service provider, users in the private cloud benefit from self-managed, authenticated data access services. However, this may lead to security issues. A critical challenge is the provision of secure and authenticated data storage for the data owner. Moreover, the data owner should be able to access the stored data and share it with others in a controlled manner. In this article, a secure and authenticated data storage, access, and sharing model is proposed for private cloud storage, which has three components. The data storage component provides the user with secure storage of information. The data-sharing component enables sharing the stored data under the control of the data owner. The data access component enables authenticated access to the cloud storage. The security analysis demonstrates that the model is secure against various attacks. The scheme is validated to be secure via the Scyther tool, BAN Logic, and in Random Oracle Model. The performance analysis regarding the computation and communication cost via simulation in OMNeT++ show that it obtains the required security goals and efficiency of computation and communication, compared to the related methods.

In recent years, Internet of Things (IoT)-enabled health monitoring wearable devices have become a trend in healthcare systems, regularly collecting vital sign data from patients and uploading them to the cloud. Through on-demand search queries, data are shared with third-party healthcare service providers to monitor patients' health status and provide timely diagnoses. To ensure privacy and security, patient health data should be encrypted before being uploaded to the cloud. The cloud can give search encryption services. However, current searchable encryption (SE) technologies still have problems with forward privacy security and verifiability. This article proposes an IoT-cloud-enabled healthcare data system incorporating a SE method with forward privacy and verifiability. By designing a trapdoor permutation function, we render the resulting output indistinguishable from meaningless random data to the adversary. Thus, the adversary cannot judge the relationship between a newly inserted record and a past search token, and therefore, the system realizes forward privacy or forward secrecy. We propose a multikeyword search verification mechanism based on a pseudo-random function. Our approach solves verifying the correctness of search results in the top-k search scenario with partial search results.Aformal security analysis proves that our scheme achieves forward privacy preservation, which can help guarantee healthcare data privacy. Additionally, a performance evaluation shows that our method is efficient and effective, providing an information security system to preserve patient privacy in IoT-enabled healthcare systems.

The Internet of things (IoT) has been widely used for various applications including medical and transportation systems, among others. Smart medical systems have become the most effective and practical solutions to provide users with low-cost, noninvasive, and long-term continuous health monitoring. Recently, Jia et al. proposed an authentication and key agreement scheme for smart medical systems based on fog computing and indicated that it is safe and can withstand a variety of known attacks. Nevertheless, we found that it consists of several flaws, including known session-specific temporary information attacks and lack of per-verification. The opponent can readily recover the session key and user identity. In this paper, we propose a secure authentication and key agreement scheme, which compensates for the imperfections of the previously proposed. For a security evaluation of the proposed authentication scheme, informal security analysis and the Burrows–Abadi–Needham (BAN) logic analysis are implemented. In addition, the ProVerif tool is used to normalize the security verification of the scheme. Finally, the performance comparisons with the former schemes show that the proposed scheme is more applicable and secure.

The wireless sensor network is a network composed of sensor nodes self‐organizing through the application of wireless communication technology. The application of wireless sensor networks (WSNs) requires high security, but the transmission of sensitive data may be exposed to the adversary. Therefore, to guarantee the security of information transmission, researchers propose numerous security authentication protocols. Recently, Wu et al. proposed a new three‐factor authentication protocol for WSNs. However, we find that their protocol cannot resist key compromise impersonation attacks and known session‐specific temporary information attacks. Meanwhile, it also violates perfect forward secrecy and anonymity. To overcome the proposed attacks, this paper proposes an enhanced protocol in which the security is verified by the formal analysis and informal analysis, Burross‐Abadii‐Needham (BAN) logic, and ProVerif tools. The comparison of security and performance proves that our protocol has higher security and lower computational overhead.

Social networks are becoming popular, with people sharing information with their friends on social networking sites. On many of these sites, shared information can be read by all of the friends; however, not all information is suitable for mass distribution and access. Although people can form communities on some sites, this feature is not yet available on all sites. Additionally, it is inconvenient to set receivers for a message when the target community is large. One characteristic of social networks is that people who know each other tend to form densely connected clusters, and connections between clusters are relatively rare. Based on this feature, community-finding algorithms have been proposed to detect communities on social networks. However, it is difficult to apply community-finding algorithms to distributed social networks. In this paper, we propose a distributed privacy control protocol for distributed social networks. By selecting only a small portion of people from a community, our protocol can transmit information to the target community.

Communication security between IoT devices is a major concern in this area, and the blockchain has raised hopes that this concern will be addressed. In the blockchain concept, the majority or even all network nodes check the validity and accuracy of exchanged data before accepting and recording them, whether this data is related to financial transactions or measurements of a sensor or an authentication message. In evaluating the validity of an exchanged data, nodes must reach a consensus in order to perform a special action, in which case the opportunity to enter and record transactions and unreliable interactions with the system is significantly reduced. Recently, in order to share and access management of IoT devices information with distributed attitude a new authentication protocol based on blockchain is proposed and it is claimed that this protocol satisfies user privacy preserving and security. However, in this paper, we show that this protocol has security vulnerabilities against secret disclosure, replay, traceability, and Token reuse attacks with the success probability of 1 and constant complexity of also 1. We also proposed an improved blockchain-based authentication protocol (IBCbAP) that has security properties such as secure access management and anonymity. We implemented IBCbAP using JavaScript programming language and Ethereum local blockchain. We also proved IBCbAP’s security both informally and formally through the Scyther tool. Our comparisons showed that IBCbAP could provide suitable security along with reasonable cost.