Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674109
Gan Tao, Zhang Heng, He Yanmin, Luo Yu
Self-supervised learning constructs supervised signals inside samples without relying on external labels, which is becoming a promising research direction. Recently, works on self-supervised learning by maximizing local-global mutual information on networks have achieved state-of-the-art performance comparable to semi-supervised graph neural networks (GNNs). However, these methods have not explored the collaborative relationship of multiple meta-path views, and the global representation is weakened by irrelevant nodes which participate in the average operation over all nodes. In this paper, a self-supervised approach based on mutual information for heterogeneous information network embedding is proposed. Specifically, it utilizes the contrast of multiple meta-path views to supervise each other, and positive samples are selected to obtain a robust global representation. Experimental results demonstrate the proposed method has competitive performance over the existing mutual-information-based ones and even outperforms some supervised learning methods.
{"title":"Contrastive Multi-View Self-Supervised Learning for Heterogeneous Information Network","authors":"Gan Tao, Zhang Heng, He Yanmin, Luo Yu","doi":"10.1109/ICCWAMTIP53232.2021.9674109","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674109","url":null,"abstract":"Self-supervised learning constructs supervised signals inside samples without relying on external labels, which is becoming a promising research direction. Recently, works on self-supervised learning by maximizing local-global mutual information on networks have achieved state-of-the-art performance comparable to semi-supervised graph neural networks (GNNs). However, these methods have not explored the collaborative relationship of multiple meta-path views, and the global representation is weakened by irrelevant nodes which participate in the average operation over all nodes. In this paper, a self-supervised approach based on mutual information for heterogeneous information network embedding is proposed. Specifically, it utilizes the contrast of multiple meta-path views to supervise each other, and positive samples are selected to obtain a robust global representation. Experimental results demonstrate the proposed method has competitive performance over the existing mutual-information-based ones and even outperforms some supervised learning methods.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674159","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674148
Liu Yuting, Ji Jing, Chen Wei
Intended to achieve a signal design with high spectrum utilization efficiency and high measurement accuracy within a limited bandwidth, in this paper, a modulation scheme of communication and navigation fusion signal is presented by combining continuous phase modulation and spectral overlay. The results show that the proposed signals perform well on the anti-multipath performance and ranging accuracy while theoretically possess good compatibility to other navigation service signals in S-band. This modulation scheme can generate flexible waveforms that provide reference to design fusion communication and navigation signals. It has a positive impact on the construction of location based services equipped with higher ranging accuracy and higher tracking sensitivity.
{"title":"Design and Performance Analysis of A Communication and Navigation Fusion Signal","authors":"Liu Yuting, Ji Jing, Chen Wei","doi":"10.1109/ICCWAMTIP53232.2021.9674148","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674148","url":null,"abstract":"Intended to achieve a signal design with high spectrum utilization efficiency and high measurement accuracy within a limited bandwidth, in this paper, a modulation scheme of communication and navigation fusion signal is presented by combining continuous phase modulation and spectral overlay. The results show that the proposed signals perform well on the anti-multipath performance and ranging accuracy while theoretically possess good compatibility to other navigation service signals in S-band. This modulation scheme can generate flexible waveforms that provide reference to design fusion communication and navigation signals. It has a positive impact on the construction of location based services equipped with higher ranging accuracy and higher tracking sensitivity.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130548390","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674119
Zhouyi Wang
Unlimited dissemination of rumors in social media has a tremendous negative impact on our society. To address this issue, many rumor verification models have been proposed and achieved reasonable verification performance. However, the imbalanced data distribution between samples heavily limit the further prosperity of the deep learning-based models. To alleviate challenges, we propose a novel hierarchical data augmentation method for the rumor verification task (termed as HDA-RV), which consists two data augmentation methods (tweet-level and thread-level data augmentation). Tweet-level data augmentation simulates the noise of text information in social media and thread-level data augmentation corresponds to the noise of the propagation structure in social networks. Experiments on the PHEME dataset show that our method can effectively alleviate the problem of data imbalance.
{"title":"Hierarchical Data Augmentation for Rumor Verification on Twitter","authors":"Zhouyi Wang","doi":"10.1109/ICCWAMTIP53232.2021.9674119","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674119","url":null,"abstract":"Unlimited dissemination of rumors in social media has a tremendous negative impact on our society. To address this issue, many rumor verification models have been proposed and achieved reasonable verification performance. However, the imbalanced data distribution between samples heavily limit the further prosperity of the deep learning-based models. To alleviate challenges, we propose a novel hierarchical data augmentation method for the rumor verification task (termed as HDA-RV), which consists two data augmentation methods (tweet-level and thread-level data augmentation). Tweet-level data augmentation simulates the noise of text information in social media and thread-level data augmentation corresponds to the noise of the propagation structure in social networks. Experiments on the PHEME dataset show that our method can effectively alleviate the problem of data imbalance.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130741078","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674089
Fadia Shah, Jianping Li, F. Shah, Y. Shah
Medical data is becoming more dense and complicated day by data. After COVID-19, the medical information is entirely expended from terabytes and petabytes. An accurate diagnosis needs a sophisticated mechanism and the support of information technology. Hadoop ecosystem is facilitating big data management for various health care applications. As dense patient history leads to better diagnosis; Hadoop architecture supports patient data accommodation, retrieval, update, and many similar functions like information assortment, information intricacy, information stockpiling, information investigation, information security, and protection.
{"title":"Hadoop with Wavelet Support for Medical Big Data","authors":"Fadia Shah, Jianping Li, F. Shah, Y. Shah","doi":"10.1109/ICCWAMTIP53232.2021.9674089","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674089","url":null,"abstract":"Medical data is becoming more dense and complicated day by data. After COVID-19, the medical information is entirely expended from terabytes and petabytes. An accurate diagnosis needs a sophisticated mechanism and the support of information technology. Hadoop ecosystem is facilitating big data management for various health care applications. As dense patient history leads to better diagnosis; Hadoop architecture supports patient data accommodation, retrieval, update, and many similar functions like information assortment, information intricacy, information stockpiling, information investigation, information security, and protection.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177055","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674063
Xiao Fei, Liao Jianping, Gao Yuan, Zhou Yue
Text classification is an important problem in natural language processing. The main task is to divide the text into different categories according to the content of the text. This article preprocesses the text in the SMS data set used to a certain extent, using the Tf-Idf model. The frequency of the text unit is counted as the feature value of the corresponding vector of the text, so that the text is converted into a vector, and then these vectors are fitted and predicted by the support vector machine algorithm.
{"title":"SMS Text Classification Model Based on Machine Learning","authors":"Xiao Fei, Liao Jianping, Gao Yuan, Zhou Yue","doi":"10.1109/ICCWAMTIP53232.2021.9674063","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674063","url":null,"abstract":"Text classification is an important problem in natural language processing. The main task is to divide the text into different categories according to the content of the text. This article preprocesses the text in the SMS data set used to a certain extent, using the Tf-Idf model. The frequency of the text unit is counted as the feature value of the corresponding vector of the text, so that the text is converted into a vector, and then these vectors are fitted and predicted by the support vector machine algorithm.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123101260","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674123
Yang Xianhua, Yang Yi, Yang Juan, Yao Han, Wang Zheng, Long Shuquan
Image multi-label classification is a critical task in the field of computer vision. The primary difficulty is that multi-label classification relies on the complex information in the image to differentiate different labels, significantly increasing the classification difficulty. We proposed a method for modifying previous models. First, we use TResNet as the benchmark model, replacing ordinary convolution with pyramid convolution in the original model and the attention mechanism in the model with the split-attention method. Then the model was trained on the VOC2007 and MS-COCO data sets. The process of selecting the model's parameters and determining the optimal modification method was demonstrated through comparative experiments. Finally, by comparing the performance of the modified model with the performance of the unmodified model, it is proved that our two modification methods can effectively improve the performance of the model. On the VOC data set, the modified model by the two methods increased by 1% and 1.6%, respectively.
{"title":"Image Multi-Label Classification Based on Pyramid Convolution and Split-Attention Mechanism","authors":"Yang Xianhua, Yang Yi, Yang Juan, Yao Han, Wang Zheng, Long Shuquan","doi":"10.1109/ICCWAMTIP53232.2021.9674123","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674123","url":null,"abstract":"Image multi-label classification is a critical task in the field of computer vision. The primary difficulty is that multi-label classification relies on the complex information in the image to differentiate different labels, significantly increasing the classification difficulty. We proposed a method for modifying previous models. First, we use TResNet as the benchmark model, replacing ordinary convolution with pyramid convolution in the original model and the attention mechanism in the model with the split-attention method. Then the model was trained on the VOC2007 and MS-COCO data sets. The process of selecting the model's parameters and determining the optimal modification method was demonstrated through comparative experiments. Finally, by comparing the performance of the modified model with the performance of the unmodified model, it is proved that our two modification methods can effectively improve the performance of the model. On the VOC data set, the modified model by the two methods increased by 1% and 1.6%, respectively.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117305061","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674116
B. L. Y. Agbley, Jianping Li, A. Haq, E. K. Bankas, Sultan Ahmad, Isaac Osei Agyemang, D. Kulevome, Waldiodio David Ndiaye, Bernard M. Cobbinah, Shoistamo Latipova
Melanoma disease analysis is increasingly approached using statistical machine learning techniques, including deep learning. These techniques require large sizes of datasets. However, health institutions are inhibited from sharing their patients' data due to concerns regarding the privacy of subjects. This paper presents a methodology that utilizes Federated Learning (FL) in ensuring the preservation of subjects' privacy during training. We fused two modalities: skin lesion images and their corresponding clinical data. The performance of the global federated model was compared with the results of a Centralized Learning (CL) scenario. The FL model is on-par with the CL model with only 0.39% and 0.73% higher F1-Score and Accuracy performances, respectively, obtained by the CL model. Through extended fine-tuning, the performance difference could be further minimized. Moreover, the FL model was 3.27% more sensitive than the CL model, hence correctly classified more positives than the CL model. Our model also obtained competitive performance when compared with other models from literature. The results indicate the capability of federated learning in effectively learning high predictive models while ensuring no training data is shared among the participating clients.
{"title":"Multimodal Melanoma Detection with Federated Learning","authors":"B. L. Y. Agbley, Jianping Li, A. Haq, E. K. Bankas, Sultan Ahmad, Isaac Osei Agyemang, D. Kulevome, Waldiodio David Ndiaye, Bernard M. Cobbinah, Shoistamo Latipova","doi":"10.1109/ICCWAMTIP53232.2021.9674116","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674116","url":null,"abstract":"Melanoma disease analysis is increasingly approached using statistical machine learning techniques, including deep learning. These techniques require large sizes of datasets. However, health institutions are inhibited from sharing their patients' data due to concerns regarding the privacy of subjects. This paper presents a methodology that utilizes Federated Learning (FL) in ensuring the preservation of subjects' privacy during training. We fused two modalities: skin lesion images and their corresponding clinical data. The performance of the global federated model was compared with the results of a Centralized Learning (CL) scenario. The FL model is on-par with the CL model with only 0.39% and 0.73% higher F1-Score and Accuracy performances, respectively, obtained by the CL model. Through extended fine-tuning, the performance difference could be further minimized. Moreover, the FL model was 3.27% more sensitive than the CL model, hence correctly classified more positives than the CL model. Our model also obtained competitive performance when compared with other models from literature. The results indicate the capability of federated learning in effectively learning high predictive models while ensuring no training data is shared among the participating clients.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129822238","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674138
Tiankai Li, Jian-Pin Li, Xi He
Fuzzing is a technology that can automatically discover the vulnerabilities of the target program. It generates test cases from the seeds and runs the target program, monitors the abnormal behavior of the target program, and then discovers test samples that can trigger the vulnerabilities. As one of the cornerstones of the fuzzing field, American Fuzzy Lop (AFL) has been widely studied by industry and academia because of its high efficiency and strong practicability. After an in-depth study of AFL and its improved version AFLFast, it is found that gray-box fuzzing tools represented by AFL are more concerned with edge coverage and do not use function call depth as one of the indicators. This paper introduces the function call depth as one of the coverage indicators, optimizes the non-deterministic mutation stage of AFL, and developed a demo deepAFL. Experiments are carried out on the LAVA-M test set. The results show that the effectiveness of seeds and the efficiency of fuzzing are improved.
{"title":"An Improvement of AFL Based On The Function Call Depth","authors":"Tiankai Li, Jian-Pin Li, Xi He","doi":"10.1109/ICCWAMTIP53232.2021.9674138","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674138","url":null,"abstract":"Fuzzing is a technology that can automatically discover the vulnerabilities of the target program. It generates test cases from the seeds and runs the target program, monitors the abnormal behavior of the target program, and then discovers test samples that can trigger the vulnerabilities. As one of the cornerstones of the fuzzing field, American Fuzzy Lop (AFL) has been widely studied by industry and academia because of its high efficiency and strong practicability. After an in-depth study of AFL and its improved version AFLFast, it is found that gray-box fuzzing tools represented by AFL are more concerned with edge coverage and do not use function call depth as one of the indicators. This paper introduces the function call depth as one of the coverage indicators, optimizes the non-deterministic mutation stage of AFL, and developed a demo deepAFL. Experiments are carried out on the LAVA-M test set. The results show that the effectiveness of seeds and the efficiency of fuzzing are improved.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129719713","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674114
Song Zhengyan
The fractal property of networks, that is, self-similarity, is a basic but important topic in the area of complex networks. In the process of studying the fractal characteristics of complex networks, the topological distance of unweighted networks is often used to represent the network. However, this ignores some local information of the network, such as the contribution of edges to node degrees. It is inconsistent with common sense. Therefore, in this paper, we propose a new algorithm which replace the traditional topological distance with the effective distance to calculate fractal dimension reasonably. Moreover, we apply this algorithm to five real networks, and the experiment results show the effectiveness and correctness of using effective distance instead of topological distance.
{"title":"Box-Covering Fractal Dimension of Complex Network: From the View of Effective Distance","authors":"Song Zhengyan","doi":"10.1109/ICCWAMTIP53232.2021.9674114","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674114","url":null,"abstract":"The fractal property of networks, that is, self-similarity, is a basic but important topic in the area of complex networks. In the process of studying the fractal characteristics of complex networks, the topological distance of unweighted networks is often used to represent the network. However, this ignores some local information of the network, such as the contribution of edges to node degrees. It is inconsistent with common sense. Therefore, in this paper, we propose a new algorithm which replace the traditional topological distance with the effective distance to calculate fractal dimension reasonably. Moreover, we apply this algorithm to five real networks, and the experiment results show the effectiveness and correctness of using effective distance instead of topological distance.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129633765","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674095
I. Obiri, Jingcong Yang, Qi Xia, Jianbin Gao
In the Internet of Things (IoT) environment, public key distribution and device authentication remain the most significant security challenges. To validate the authenticity of the identity of IoT devices, existing solutions depend on Public Key Infrastructure (PKI) backed by Certificate Authorities (CA). CA-based PKI has flaws in terms of a single point of failure and certificate transparency. While some blockchain-based PKI solutions exist, they either have a high storage overhead or require a lot of cryptographic computations in the smart contract, which can exceed the transaction size limit on the blockchain network. Hence, we propose a sovereign PKI for IoT devices based on blockchain technology, in which individual controls and maintains the public and private keys for the IoT devices he or she owns. Public keys are kept in a decentralized key store database (DKSB). The blockchain serves as the ground proof for authenticating identities (public keys) on the DKSB. Cryptographic operations like identity authentication are done off-chain without incurring transaction fees.
{"title":"A Sovereign PKI for IoT Devices Based on the Blockchain Technology","authors":"I. Obiri, Jingcong Yang, Qi Xia, Jianbin Gao","doi":"10.1109/ICCWAMTIP53232.2021.9674095","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674095","url":null,"abstract":"In the Internet of Things (IoT) environment, public key distribution and device authentication remain the most significant security challenges. To validate the authenticity of the identity of IoT devices, existing solutions depend on Public Key Infrastructure (PKI) backed by Certificate Authorities (CA). CA-based PKI has flaws in terms of a single point of failure and certificate transparency. While some blockchain-based PKI solutions exist, they either have a high storage overhead or require a lot of cryptographic computations in the smart contract, which can exceed the transaction size limit on the blockchain network. Hence, we propose a sovereign PKI for IoT devices based on blockchain technology, in which individual controls and maintains the public and private keys for the IoT devices he or she owns. Public keys are kept in a decentralized key store database (DKSB). The blockchain serves as the ground proof for authenticating identities (public keys) on the DKSB. Cryptographic operations like identity authentication are done off-chain without incurring transaction fees.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130938309","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}
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