Pub Date : 2024-06-13DOI: 10.1109/TBDATA.2024.3414321
Mengting Diao;Zhongbao Zhang;Sen Su;Shuai Gao;Huafeng Cao;Junda Ye
User profiling has very important applications for many downstream tasks. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Fortunately, we observed that similar users have similar behavior patterns in different social networks. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design two efficient frameworks for User Profile transferring acrOss Networks, i.e., UPON and E-UPON. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model to find user dependencies within and between domains (i.e., social networks). We then design a dual-domain weighted adversarial learning method to address the domain shift problem existing in the transferring procedure. In E-UPON, we optimize UPON in terms of computational complexity and memory. Specifically, we design a mini-cluster gradient descent based graph representation algorithm to shrink the searching space and ensure parallel computation. Then we use an adaptive cluster matching method to adjust the clusters of users. Experimental results on Twitter-Foursquare dataset demonstrate that UPON and E-UPON outperform the state-of-the-art models.
{"title":"Efficiently Transfer User Profile Across Networks","authors":"Mengting Diao;Zhongbao Zhang;Sen Su;Shuai Gao;Huafeng Cao;Junda Ye","doi":"10.1109/TBDATA.2024.3414321","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3414321","url":null,"abstract":"User profiling has very important applications for many downstream tasks. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Fortunately, we observed that similar users have similar behavior patterns in different social networks. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design two efficient frameworks for User Profile transferring acrOss Networks, i.e., UPON and E-UPON. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model to find user dependencies within and between domains (i.e., social networks). We then design a dual-domain weighted adversarial learning method to address the domain shift problem existing in the transferring procedure. In E-UPON, we optimize UPON in terms of computational complexity and memory. Specifically, we design a mini-cluster gradient descent based graph representation algorithm to shrink the searching space and ensure parallel computation. Then we use an adaptive cluster matching method to adjust the clusters of users. Experimental results on Twitter-Foursquare dataset demonstrate that UPON and E-UPON outperform the state-of-the-art models.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"271-285"},"PeriodicalIF":7.5,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.1109/TBDATA.2024.3409939
Fahd N. Al-Wesabi;Marwa Obayya;Jamal Alsamri;Rana Alabdan;Nojood O Aljehane;Sana Alazwari;Fahad F. Alruwaili;Manar Ahmed Hamza;A Swathi
The Web of Things (WoT) is a network that facilitates the formation and distribution of information its users make. Young people nowadays, digital natives, have no trouble relating to others or joining groups online since they have grown up in a world where new technology has pushed communications to a nearly real-time level. Shared private messages, rumours, and sexual comments are all examples of online harassment that have led to several recent cases worldwide. Therefore, academics have been more interested in finding ways to recognise bullying conduct on these platforms. The effects of cyberbullying, a terrible form of online misbehaviour, are distressing. It takes several documents, but the text is predominant on social networks. Intelligent systems are required for the automatic detection of such occurrences. Most previous research has used standard machine-learning techniques to tackle this issue. The increasing pervasiveness of cyberbullying in WoT and other social media platforms is a significant cause for worry that calls for robust responses to prevent further harm. This study offers a unique method of leveraging the deep learning (DL) model binary coyote optimization-based Convolutional Neural Network (BCNN) in social networks to identify and classify cyberbullying. An essential part of this method is the combination of DL-based abuse detection and feature subset selection. To efficiently detect and address cases of cyberbullying via social media, the proposed system incorporates many crucial steps, including preprocessing, feature selection, and classification. A binary coyote optimization (BCO)-based feature subset selection method is presented to enhance classification efficiency. To improve the accuracy of cyberbullying categorization, the BCO algorithm efficiently chooses a selection of key characteristics. Cyberbullying must be tracked and classified across all internet channels, and Convolutional Neural Network (CNN) is constructed. With a best-case accuracy of 99.5% on Formspring, 99.7% on Twitter, and 99.3% on Wikipedia, the suggested algorithm successfully identified the vast majority of cyberbullying content.
{"title":"Automatic Recognition of Cyberbullying in the Web of Things and social media using Deep Learning Framework","authors":"Fahd N. Al-Wesabi;Marwa Obayya;Jamal Alsamri;Rana Alabdan;Nojood O Aljehane;Sana Alazwari;Fahad F. Alruwaili;Manar Ahmed Hamza;A Swathi","doi":"10.1109/TBDATA.2024.3409939","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3409939","url":null,"abstract":"The Web of Things (WoT) is a network that facilitates the formation and distribution of information its users make. Young people nowadays, digital natives, have no trouble relating to others or joining groups online since they have grown up in a world where new technology has pushed communications to a nearly real-time level. Shared private messages, rumours, and sexual comments are all examples of online harassment that have led to several recent cases worldwide. Therefore, academics have been more interested in finding ways to recognise bullying conduct on these platforms. The effects of cyberbullying, a terrible form of online misbehaviour, are distressing. It takes several documents, but the text is predominant on social networks. Intelligent systems are required for the automatic detection of such occurrences. Most previous research has used standard machine-learning techniques to tackle this issue. The increasing pervasiveness of cyberbullying in WoT and other social media platforms is a significant cause for worry that calls for robust responses to prevent further harm. This study offers a unique method of leveraging the deep learning (DL) model binary coyote optimization-based Convolutional Neural Network (BCNN) in social networks to identify and classify cyberbullying. An essential part of this method is the combination of DL-based abuse detection and feature subset selection. To efficiently detect and address cases of cyberbullying via social media, the proposed system incorporates many crucial steps, including preprocessing, feature selection, and classification. A binary coyote optimization (BCO)-based feature subset selection method is presented to enhance classification efficiency. To improve the accuracy of cyberbullying categorization, the BCO algorithm efficiently chooses a selection of key characteristics. Cyberbullying must be tracked and classified across all internet channels, and Convolutional Neural Network (CNN) is constructed. With a best-case accuracy of 99.5% on Formspring, 99.7% on Twitter, and 99.3% on Wikipedia, the suggested algorithm successfully identified the vast majority of cyberbullying content.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"259-270"},"PeriodicalIF":7.5,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-29DOI: 10.1109/TBDATA.2024.3394700
Prakash Tekchandani;Abhishek Bisht;Ashok Kumar Das;Neeraj Kumar;Marimuthu Karuppiah;Pandi Vijayakumar;Youngho Park
With the rise of Big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in Big Data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current Big Data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.
{"title":"Blockchain-Enabled Secure Collaborative Model Learning Using Differential Privacy for IoT-Based Big Data Analytics","authors":"Prakash Tekchandani;Abhishek Bisht;Ashok Kumar Das;Neeraj Kumar;Marimuthu Karuppiah;Pandi Vijayakumar;Youngho Park","doi":"10.1109/TBDATA.2024.3394700","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3394700","url":null,"abstract":"With the rise of Big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in Big Data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current Big Data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"141-156"},"PeriodicalIF":7.5,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-30DOI: 10.1109/TBDATA.2024.3407510
Xiaoge Deng;Dongsheng Li;Tao Sun;Xicheng Lu
Gradient-based optimization methods implemented on distributed computing architectures are increasingly used to tackle large-scale machine learning applications. A key bottleneck in such distributed systems is the high communication overhead for exchanging information, such as stochastic gradients, between workers. The inherent causes of this bottleneck are the frequent communication rounds and the full model gradient transmission in every round. In this study, we present SASG, a communication-efficient distributed algorithm that enjoys the advantages of sparse communication and adaptive aggregated stochastic gradients. By dynamically determining the workers who need to communicate through an adaptive aggregation rule and sparsifying the transmitted information, the SASG algorithm reduces both the overhead of communication rounds and the number of communication bits in the distributed system. For the theoretical analysis, we introduce an important auxiliary variable and define a new Lyapunov function to prove that the communication-efficient algorithm is convergent. The convergence result is identical to the sublinear rate of stochastic gradient descent, and our result also reveals that SASG scales well with the number of distributed workers. Finally, experiments on training deep neural networks demonstrate that the proposed algorithm can significantly reduce communication overhead compared to previous methods.
{"title":"Communication-Efficient Distributed Learning via Sparse and Adaptive Stochastic Gradient","authors":"Xiaoge Deng;Dongsheng Li;Tao Sun;Xicheng Lu","doi":"10.1109/TBDATA.2024.3407510","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407510","url":null,"abstract":"Gradient-based optimization methods implemented on distributed computing architectures are increasingly used to tackle large-scale machine learning applications. A key bottleneck in such distributed systems is the high communication overhead for exchanging information, such as stochastic gradients, between workers. The inherent causes of this bottleneck are the frequent communication rounds and the full model gradient transmission in every round. In this study, we present SASG, a communication-efficient distributed algorithm that enjoys the advantages of sparse communication and adaptive aggregated stochastic gradients. By dynamically determining the workers who need to communicate through an adaptive aggregation rule and sparsifying the transmitted information, the SASG algorithm reduces both the overhead of communication rounds and the number of communication bits in the distributed system. For the theoretical analysis, we introduce an important auxiliary variable and define a new Lyapunov function to prove that the communication-efficient algorithm is convergent. The convergence result is identical to the sublinear rate of stochastic gradient descent, and our result also reveals that SASG scales well with the number of distributed workers. Finally, experiments on training deep neural networks demonstrate that the proposed algorithm can significantly reduce communication overhead compared to previous methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"234-246"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-30DOI: 10.1109/TBDATA.2024.3407573
Lele Cao;Vilhelm von Ehrenheim;Mark Granroth-Wilding;Richard Anselmo Stahl;Andrew McCornack;Armin Catovic;Dhiana Deva Cavalcanti Rocha
In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis, and mergers and acquisitions. We propose and publish a knowledge graph, named CompanyKG, to represent and learn diverse company features and relations. Specifically, 1.17 million companies are represented as nodes enriched with company description embeddings; and 15 different inter-company relations result in 51.06 million weighted edges. To enable a comprehensive assessment of methods for company similarity quantification, we have devised and compiled three evaluation tasks with annotated test sets: similarity prediction, competitor retrieval and similarity ranking. We present extensive benchmarking results for 11 reproducible predictive methods categorized into three groups: node-only, edge-only, and node+edge. To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.
{"title":"CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification","authors":"Lele Cao;Vilhelm von Ehrenheim;Mark Granroth-Wilding;Richard Anselmo Stahl;Andrew McCornack;Armin Catovic;Dhiana Deva Cavalcanti Rocha","doi":"10.1109/TBDATA.2024.3407573","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407573","url":null,"abstract":"In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis, and mergers and acquisitions. We propose and publish a knowledge graph, named CompanyKG, to represent and learn diverse company features and relations. Specifically, 1.17 million companies are represented as nodes enriched with company description embeddings; and 15 different inter-company relations result in 51.06 million weighted edges. To enable a comprehensive assessment of methods for company similarity quantification, we have devised and compiled three evaluation tasks with annotated test sets: similarity prediction, competitor retrieval and similarity ranking. We present extensive benchmarking results for 11 reproducible predictive methods categorized into three groups: node-only, edge-only, and node+edge. To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"247-258"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-26DOI: 10.1109/TBDATA.2024.3378098
Wen Zhang;Haitao Fu;Huan Wang;Zhiguo Gong;Pan Zhou;Di Wang
Nowadays, as human activities are shifting to social media, fake news detection has been a crucial problem. Existing methods ignore the classification difference in online news and cannot take full advantage of multi-classification knowledges. For example, when coping with a post “A mouse is frightened by a cat,” a model that learns “computer” knowledge tends to misunderstand “mouse” and give a fake label, but a model that learns “animal” knowledge tends to give a true label. Therefore, this research proposes a multi-classification division-aggregation framework to detect fake news, named $CKA$, which innovatively learns classification knowledges during training stages and aggregates them during prediction stages. It consists of three main components: a news characterizer, an ensemble coordinator, and a truth predictor. The news characterizer is responsible for extracting news features and obtaining news classifications. Cooperating with the news characterizer, the ensemble coordinator generates classification-specifical models for the maximum reservation of classification knowledges during the training stage, where each classification-specifical model maximizes the detection performance of fake news on corresponding news classifications. Further, to aggregate the classification knowledges during the prediction stage, the truth predictor uses the truth discovery technology to aggregate the predictions from different classification-specifical models based on reliability evaluation of classification-specifical models. Extensive experiments prove that our proposed $CKA$ outperforms state-of-the-art baselines in fake news detection.
{"title":"3A Multi-Classification Division-Aggregation Framework for Fake News Detection","authors":"Wen Zhang;Haitao Fu;Huan Wang;Zhiguo Gong;Pan Zhou;Di Wang","doi":"10.1109/TBDATA.2024.3378098","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3378098","url":null,"abstract":"Nowadays, as human activities are shifting to social media, fake news detection has been a crucial problem. Existing methods ignore the classification difference in online news and cannot take full advantage of multi-classification knowledges. For example, when coping with a post “A mouse is frightened by a cat,” a model that learns “computer” knowledge tends to misunderstand “mouse” and give a fake label, but a model that learns “animal” knowledge tends to give a true label. Therefore, this research proposes a multi-classification division-aggregation framework to detect fake news, named <inline-formula><tex-math>$CKA$</tex-math></inline-formula>, which innovatively learns classification knowledges during training stages and aggregates them during prediction stages. It consists of three main components: a news characterizer, an ensemble coordinator, and a truth predictor. The news characterizer is responsible for extracting news features and obtaining news classifications. Cooperating with the news characterizer, the ensemble coordinator generates classification-specifical models for the maximum reservation of classification knowledges during the training stage, where each classification-specifical model maximizes the detection performance of fake news on corresponding news classifications. Further, to aggregate the classification knowledges during the prediction stage, the truth predictor uses the truth discovery technology to aggregate the predictions from different classification-specifical models based on reliability evaluation of classification-specifical models. Extensive experiments prove that our proposed <inline-formula><tex-math>$CKA$</tex-math></inline-formula> outperforms state-of-the-art baselines in fake news detection.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"130-140"},"PeriodicalIF":7.5,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-22DOI: 10.1109/TBDATA.2024.3381017
Zitai Qiu;Jia Wu;Jian Yang;Xing Su;Charu Aggarwal
Social events reflect the dynamics of society and, here, natural disasters and emergencies receive significant attention. The timely detection of these events can provide organisations and individuals with valuable information to reduce or avoid losses. However, due to the complex heterogeneities of the content and structure of social media, existing models can only learn limited information; large amounts of semantic and structural information are ignored. In addition, due to high labour costs, it is rare for social media datasets to include high-quality labels, which also makes it challenging for models to learn information from social media. In this study, we propose two hyperbolic graph representation-based methods for detecting social events from heterogeneous social media environments. For cases where a dataset has labels, we design a Hyperbolic Social Event Detection (HSED) model that converts complex social information into a unified social message graph. This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space. For cases where the dataset is unlabelled, we design an Unsupervised Hyperbolic Social Event Detection (UHSED). This model is based on the HSED model but includes graph contrastive learning to make it work in unlabelled scenarios. Extensive experiments demonstrate the superiority of the proposed approaches.
{"title":"Heterogeneous Social Event Detection via Hyperbolic Graph Representations","authors":"Zitai Qiu;Jia Wu;Jian Yang;Xing Su;Charu Aggarwal","doi":"10.1109/TBDATA.2024.3381017","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3381017","url":null,"abstract":"Social events reflect the dynamics of society and, here, natural disasters and emergencies receive significant attention. The timely detection of these events can provide organisations and individuals with valuable information to reduce or avoid losses. However, due to the complex heterogeneities of the content and structure of social media, existing models can only learn limited information; large amounts of semantic and structural information are ignored. In addition, due to high labour costs, it is rare for social media datasets to include high-quality labels, which also makes it challenging for models to learn information from social media. In this study, we propose two hyperbolic graph representation-based methods for detecting social events from heterogeneous social media environments. For cases where a dataset has labels, we design a <bold><u>H</u></b>yperbolic <bold><u>S</u></b>ocial <bold><u>E</u></b>vent <bold><u>D</u></b>etection (HSED) model that converts complex social information into a unified social message graph. This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space. For cases where the dataset is unlabelled, we design an <bold><u>U</u></b>nsupervised <bold><u>H</u></b>yperbolic <bold><u>S</u></b>ocial <bold><u>E</u></b>vent <bold><u>D</u></b>etection (UHSED). This model is based on the HSED model but includes graph contrastive learning to make it work in unlabelled scenarios. Extensive experiments demonstrate the superiority of the proposed approaches.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"115-129"},"PeriodicalIF":7.5,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1109/TBDATA.2024.3403374
Haiyong Bao;Zhehong Wang;Rongxing Lu;Cheng Huang;Beibei Li
Spatial crowdsourcing is a distributed computing paradigm that utilizes the collective intelligence of workers to perform complex tasks. How to achieve privacy-preserving task assignment in spatial crowdsourcing applications has been a popular research area. However, most of the existing task assignment schemes may reveal private and sensitive information of tasks or workers. Few schemes can support task assignment based on different attributes simultaneously, such as spatial, interest, etc. To study the above themes, in this paper, we propose one privacy-preserving task assignment scheme with multi-threshold range search for spatial crowdsourcing applications (TAMT). Specifically, we first define euclidean distance-based location search and Hamming distance-based interest search, which map the demands of the tasks and the interests of the workers into the binary vectors. Second, we deploy PKD-tree to index the task data leveraging the pivoting techniques and the triangular inequality of euclidean distance, and propose an efficient multi-threshold range search algorithm based on matrix encryption and decomposition technology. Furthermore, based on DT-PKC, we introduce a ciphertext-based secure comparison protocol to support multi-threshold range search for spatial crowdsourcing applications. Finally, comprehensive security analysis proves that our proposed TAMT is privacy-preserving. Meanwhile, theoretical analysis and experimental evaluation demonstrate that TAMT is practical and efficient.
{"title":"TAMT: Privacy-Preserving Task Assignment With Multi-Threshold Range Search for Spatial Crowdsourcing Applications","authors":"Haiyong Bao;Zhehong Wang;Rongxing Lu;Cheng Huang;Beibei Li","doi":"10.1109/TBDATA.2024.3403374","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3403374","url":null,"abstract":"Spatial crowdsourcing is a distributed computing paradigm that utilizes the collective intelligence of workers to perform complex tasks. How to achieve privacy-preserving task assignment in spatial crowdsourcing applications has been a popular research area. However, most of the existing task assignment schemes may reveal private and sensitive information of tasks or workers. Few schemes can support task assignment based on different attributes simultaneously, such as spatial, interest, etc. To study the above themes, in this paper, we propose one privacy-preserving task assignment scheme with multi-threshold range search for spatial crowdsourcing applications (TAMT). Specifically, we first define euclidean distance-based location search and Hamming distance-based interest search, which map the demands of the tasks and the interests of the workers into the binary vectors. Second, we deploy PKD-tree to index the task data leveraging the pivoting techniques and the triangular inequality of euclidean distance, and propose an efficient multi-threshold range search algorithm based on matrix encryption and decomposition technology. Furthermore, based on DT-PKC, we introduce a ciphertext-based secure comparison protocol to support multi-threshold range search for spatial crowdsourcing applications. Finally, comprehensive security analysis proves that our proposed TAMT is privacy-preserving. Meanwhile, theoretical analysis and experimental evaluation demonstrate that TAMT is practical and efficient.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"208-220"},"PeriodicalIF":7.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A vulnerability or error in a smart contract will lead to serious consequences including loss of assets and leakage of user privacy. Established smart contract vulnerability detection tools define vulnerabilities through symbolic execution, fuzz testing, and other methods requiring extremely specialized security knowledge. Even so, with the development of vulnerability exploitation techniques, vulnerability detection tools customized by experts cannot cope with the deformation of existing vulnerabilities or unknown vulnerabilities. The vulnerability detection based on machine learning developed in recent years studies vulnerabilities from different dimensions and designs corresponding models to achieve a high detection rate. However, these methods usually only focus on some features of smart contracts, or the model itself does not have universality. Experimental results on the publicly large-scale dataset SmartBugs-Wild demonstrate that this paper's method not only outperforms existing methods in several metrics, but also is scalable, general, and requires less domain knowledge, providing a new idea for the development of smart contract vulnerability detection.
{"title":"A Universal and Efficient Multi-Modal Smart Contract Vulnerability Detection Framework for Big Data","authors":"Wenjuan Lian;Zikang Bao;Xinze Zhang;Bin Jia;Yang Zhang","doi":"10.1109/TBDATA.2024.3403376","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3403376","url":null,"abstract":"A vulnerability or error in a smart contract will lead to serious consequences including loss of assets and leakage of user privacy. Established smart contract vulnerability detection tools define vulnerabilities through symbolic execution, fuzz testing, and other methods requiring extremely specialized security knowledge. Even so, with the development of vulnerability exploitation techniques, vulnerability detection tools customized by experts cannot cope with the deformation of existing vulnerabilities or unknown vulnerabilities. The vulnerability detection based on machine learning developed in recent years studies vulnerabilities from different dimensions and designs corresponding models to achieve a high detection rate. However, these methods usually only focus on some features of smart contracts, or the model itself does not have universality. Experimental results on the publicly large-scale dataset SmartBugs-Wild demonstrate that this paper's method not only outperforms existing methods in several metrics, but also is scalable, general, and requires less domain knowledge, providing a new idea for the development of smart contract vulnerability detection.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"190-207"},"PeriodicalIF":7.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1109/TBDATA.2024.3403394
Jalal Ghadermazi;Ankit Shah;Nathaniel D. Bastian
Machine learning (ML) and deep learning (DL) advancements have greatly enhanced anomaly detection of network intrusion detection systems (NIDS) by empowering them to analyze Big Data and extract patterns. ML/DL-based NIDS are trained using either flow-based or packet-based features. Flow-based NIDS are suitable for offline traffic analysis, while packet-based NIDS can analyze traffic and detect attacks in real-time. Current packet-based approaches analyze packets independently, overlooking the sequential nature of network communication. This results in biased models that exhibit increased false negatives and positives. Additionally, most literature-proposed packet-based NIDS capture only payload data, neglecting crucial information from packet headers. This oversight can impair the ability to identify header-level attacks, such as denial-of-service attacks. To address these limitations, we propose a novel artificial intelligence-enabled methodological framework for packet-based NIDS that effectively analyzes header and payload data and considers temporal connections among packets. Our framework transforms sequential packets into two-dimensional images. It then develops a convolutional neural network-based intrusion detection model to process these images and detect malicious activities. Through experiments using publicly available big datasets, we demonstrate that our framework is able to achieve high detection rates of 97.7% to 99% across different attack types and displays promising resilience against adversarial examples.
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