2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)最新文献

{"title":"Multi-Party Private Set Intersection in Vertical Federated Learning","authors":"Linpeng Lu, Ning Ding","doi":"10.1109/TrustCom50675.2020.00098","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00098","url":null,"abstract":"Vertical federated learning (VFL) is a privacy-preserving machine learning framework in which the training dataset is vertically partitioned and distributed over multiple parties, i.e., for each sample each party only possesses some attributes of it. In this paper we address the problem of computing private set intersection (PSI) in VLF, in which a private set denotes the data possessed by a party satisfying some distinguishing constraint. This problem actually asks how the parties jointly compute the common IDs of their private sets, which plays a key role in many learning tasks such as Decision Tree Learning. Currently all known PSI protocols, to our knowledge, either involve expensive cryptographic operations, or are designed for the two-party scenario originally which will leak privacy-sensitive information in multi-party scenario if applied to each pair of parties gradually. In this paper we propose a new multi-party PSI protocol in VFL, which can even handle the case that some parties drop out in the running of the protocol. Our protocol achieves the security that any coalition of corrupted parties, which number is less than a threshold, cannot learn any secret information of honest parties, thus realizing the goal of preserving the privacy of the involved parties. Moreover, it only relies on light cryptographic primitives (i.e. PRGs) and thus works more efficiently compared to the known protocols, especially when the sample number of dataset gets larger and larger. Our starting point to solve the PSI problem in VFL is to reduce it to computing the AND operation of multiple bit-vectors, each held by one party, which are used to identify parties' private sets in their data. Then our main technical contribution is to present an efficient protocol for summing up these vectors, called MulSUM, and then adapt it to a desired protocol, called MulAND, to compute the AND of these vectors, which result actually identifies the intersection of private sets of all (online) parties, thus accomplishing the PSI issue.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127957142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of 5G, VANETs and Blockchain Technology","authors":"Muhammad Arif, W. Balzano, A. Fontanella, Silvia Stranieri, Guojun Wang, X. Xing","doi":"10.1109/TrustCom50675.2020.00275","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00275","url":null,"abstract":"The global internet of vehicles market is growing rapidly, it is estimated to increase significantly its value by the next few years. Vehicular Ad hoc Networks (VANETs) has a central role in the development of Intelligent Transportation System, since vehicles can communicate with each other. This paper proposes a model integrating both 5G and Blockchain for vehicular ad-hoc network management. This choice is motivated by the need of guaranteeing secure and reliable information exchange between vehicles. 5G provides low latency communication improving both V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) connections increasing considerably their trustworthiness. On the other side, BlockChain offers a distributed ledger, enhancing security and data reliability. These technologies together with VANETs mechanism can provide multiple new opportunities and uses, such as automating braking system. In this work, not only we provide a complete overview of these technologies, but also we suggest a new research topic, based on the integration of such technologies with VANETs environment, to obtain a very robust network, and hence a safer traffic management.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132327878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TrustyShare: A Sharing Scheme using ARM TrustZone","authors":"Yongkang Wu, Langyue He, Yiwei Shan, Pengcheng Zhang, Min He, Zhi Yang","doi":"10.1109/TrustCom50675.2020.00239","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00239","url":null,"abstract":"As the applications on smartphones and tablets are getting richer and more powerful, people are more willing to do their daily works and entertainments on mobile devices. Sharing and collaborating on these devices in a convenient and safe way has become a growing demand in modern life. However, due to the lack of a flexible access control strategy of the official online services, typical applications such as email, video and music do not support this kind of sharing scheme. In this paper, we propose TrustyShare to provide a general sharing solution based on the ARM TrustZone technology which has a extremely wide range of applications in mobile terminal devices and Internet of Things devices. Our solution allows users to share services they enjoy without the official support from these services, while avoiding the disclosure of their private credentials. In addition, credential owners have full control on how and when others can use their services through flexible access control policies. We implement our solution on OP-TEE platform, and our experimental results demonstrate that TrustyShare works well with good performance.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atrial Fibrillation Classification Using Convolutional Neural Networks and Time Domain Features of ECG Sequence","authors":"Mixue Deng, Lishen Qiu, Hongqing Wang, Wei Shi, Lirong Wang","doi":"10.1109/TrustCom50675.2020.00201","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00201","url":null,"abstract":"Atrial fibrillation is a serious cardiovascular disease. It is the main cause of heart disease such as myocardial infarction. ECG based atrial fibrillation detection is very important for clinical diagnosis. In this paper, a method based on one-dimensional CNN and time domain features of ECG sequence is proposed to detect atrial fibrillation. The ECG data used came from the MIT-BIH atrial fibrillation database. The first step is to filter out the noise interference in ECG. In the second step, ECG signals were segmented into seven heart beats. In the third step, 8 features are extracted based on the time domain features of ECG sequence to form the feature vector (size 1*8). In the fourth step, the one-hot label (1*2) output by the convolutional neural network was combined with the extracted time domain features (size 1*8) to obtain a total of 10 dimensional features. In the fifth step, the extracted 10-dimensional features are normalized and then put into the SVM classifier. The experimental results show that the sensitivity, specificity and total accuracy of the proposed algorithm are 99.07%, 97.05% and 98.03%, respectively. This algorithm has great potential to help doctors and reduce mortality.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134270801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors affecting trust in the autonomous vehicle: A survey of primary school students and parent perceptions","authors":"Yingying Ma, Shuo Li, Shaotian Qin, Yue Qi","doi":"10.1109/TrustCom50675.2020.00277","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00277","url":null,"abstract":"With the rapid development of autonomous driving technology, autonomous vehicles are expected to perform all driving functions under certain conditions. In fact, some people have even proposed the concept of autonomous school buses. Accordingly, autonomous vehicles exhibit the great potential to become not only the primary alternative to human-driven vehicles during a child's journey to school, but also the vehicles used by today's children as they enter adulthood. However, the acceptance of such vehicles depends on both the children and their parents' trust in this new technology. Hence, this study explores the relationships among perceived usefulness, defects, and risks as well as negative emotions and degree of trust in autonomous vehicles. Based on an online survey of 131 primary school students and 133 parents, the current study concludes that the perceived benefits, perceived risks, and emotional responses influence people's trust in this new technology. Moreover, the results of a regression analysis find two critical factors that influence trust in autonomous vehicles, namely, perceived risk of traffic safety and perceived defects in the vehicle. Additionally, while perceived usefulness is an exclusive predictor of adults' trust, negative emotions only significantly predict the trust of children in the vehicle. For trust in autonomous vehicles, these primary school students share similar perceptions with those of their parents. To our knowledge, the present study is the first to compare people's trust in autonomous vehicles between primary school students and their parents. These findings may inspire the design and promotion of autonomous vehicles for school journeys, thus resulting in increasingly more families trusting and accepting this new technology.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128931364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fined-grained Aspect Extraction from Online Reviews for Decision Support","authors":"Zhaoli Liu, Qindong Sun, Zhihai Yang, Kun Jiang, Jinpei Yan","doi":"10.1109/TrustCom50675.2020.00210","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00210","url":null,"abstract":"With the flourish of the Web 2.0, online reviews offer valuable information for customers and businesses. Deep investigation on the online reviews can help the businesses understand customers and their needs, which can assist decision making in product design and marketing. However, the massive records with irregular structure and ambiguous words pose great challenges for online review analysis. In this paper, we focus on the movie reviews and propose a framework to mine the aspect-based opinions, and utilize the results for decision making support. Based on the different sentence characteristics of movie reviews collected from Douban, the most popular movie community in China, we divide the reviews into two categories, short reviews and long reviews. Firstly, we develop different methods to extract the fine-grained aspects including the global and local aspects from the short reviews and long reviews respectively. Secondly, a lexical updating algorithm is proposed to identify the opinion words towards different aspects. In contrast to most studies that focus on determining the overall sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to mine both the various aspects and their corresponding opinions of a movie. Finally, based on the positive and negative opinions towards different aspects, the producers can improve the marketing strategy and future products. Experimental results based on the data collected from Douban verify the efficiency and accuracy of the developed methods.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malware Detection Based on Term Frequency Analysis of GPRs Features","authors":"Fang Li, Ziyuan Zhu, Chao Yan, Bowen Chen, Dan Meng","doi":"10.1109/TrustCom50675.2020.00037","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00037","url":null,"abstract":"Recently, low-level hardware micro-architecture features are widely used for malware detection, but they always have redundant information, which will inevitably affect malware detection. To address the above problem, this paper proposed a novel dynamic analysis method to detect malware. The feature matrices are first extracted from the General-Purpose Registers (GPRs) that contain a large amount of valuable but redundant information. To reduce the feature dimension, Term Frequency-Inverse Document Frequency (TF-IDF) technique is then used to select the discriminative information from feature matrices. With the selected features, this paper also designs an ensemble learning model for malware detection. Experimental results show that the proposed method performs better than other state-of-art methods.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Four-Feature Keyword Extraction Algorithm Based on Word Length Priority ratio","authors":"Hui Kang, Lingfeng Lu, H. Su","doi":"10.1109/TrustCom50675.2020.00203","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00203","url":null,"abstract":"With the rapid development of Internet technology and the advent of the information age, it has become a research hotspot to obtain key information from numerous data. Due to the diversity and irregularity of network data, it is difficult for people to find the literature they want, especially knowledge scholars working in the frontier field of science and technology, who have higher requirements on the accuracy and efficiency of literature keyword extraction than ordinary people. The feature values selected by the current keyword extraction algorithm are usually limited to word frequency and word length, which is incomplete and affects the accuracy of the algorithm. Given this phenomenon, this paper, by comparing with TF-IDF and KEA algorithm, define the concept of word length priority ratio, and applies this concept to the calculation of word length-weight, proposes a four-feature keyword extraction algorithm (WPR-TOC algorithm) based on word frequency, word length, word position and the degree of association between words. Through experiments, compared with the KEA algorithm, KEA++ algorithm, and four features extraction algorithm, the precision of the WPR-TOC algorithm is improved by 40%, 30%, and 10% respectively, and the recall rate is also increased by 40%, 30%, and 10% respectively.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131675158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RevBloc: A Blockchain-based Secure Customer Review System","authors":"Xiuli Fang, M. R. Asghar","doi":"10.1109/TrustCom50675.2020.00168","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00168","url":null,"abstract":"Customer reviews enable customers to share their experiences with others, which allow potential customers to know more about products and consume products with confidence. However, online product sellers and service providers could manipulate customer reviews, such as adding fake positive reviews and removing negative customer reviews, to support their business. Manipulated reviews could result in distorting the original content of customer reviews and misleading customers. State-of-the-art solutions lack a customer review system that is secure, efficient, and usable. In this paper, we propose RevBloc to provide a customer review system with a high level of security, efficiency, and usability. RevBloc is based on blockchain technology that enables customer reviews to be preserved in a distributed ledger, thus a single or subset of malicious parties cannot manipulate the reviews. To show the feasibility of our approach, we implement a proof-of-concept prototype of RevBloc and report its performance.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115550965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting Homograph Attack Classification Using Ensemble Learning and N-gram Model","authors":"Tran Thao Phuong, Hoang-Quoc Nguyen-Son, R. Yamaguchi, Toshiyuki Nakata","doi":"10.1109/TrustCom50675.2020.00271","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00271","url":null,"abstract":"A visual homograph attack is a way that the attacker deceives the web users about which domain they are visiting by exploiting forged domains that look similar to the genuine domains. T. Thao et al. (IFIP SEC'19) proposed a homograph classification by applying conventional supervised learning algorithms on the features extracted from a single-character-based Structural Similarity Index (SSIM). This paper aims to improve the classification accuracy by combining their SSIM features with 199 features extracted from a N-gram model and applying advanced ensemble learning algorithms. The experimental result showed that our proposed method could enhance even 1.81% of accuracy and reduce 2.15% of false-positive rate. Furthermore, existing work applied machine learning on some features without being able to explain why applying it can improve the accuracy. Even though the accuracy could be improved, understanding the ground-truth is also crucial. Therefore, in this paper, we conducted an error empirical analysis and could obtain several findings behind our proposed approach.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114406531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}