Pub Date : 2021-10-01DOI: 10.1109/NaNA53684.2021.00045
Mingbo Pan, Weibin Su, Yikai Wang
In the background of edge computing becoming an important technology of China’s new infrastructure construction, it is necessary for colleges and universities to set up a new professional curriculum system for edge computing. It is construction and learning from the refinement of artificial intelligence which is easier to industrialize. Through the interdisciplinary integration and optimization construction of the curriculum, this course system is suitable for universities. According to developed course content, design the teaching schedule, and then, based on this scheme, we will cultivate edge computing research and application-oriented talents, and support the transformation and upgrading of industrial automation to intelligent.
{"title":"Review of Research on the Curriculum for Artificial Intelligence and Industrial Automation based on Edge Computing","authors":"Mingbo Pan, Weibin Su, Yikai Wang","doi":"10.1109/NaNA53684.2021.00045","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00045","url":null,"abstract":"In the background of edge computing becoming an important technology of China’s new infrastructure construction, it is necessary for colleges and universities to set up a new professional curriculum system for edge computing. It is construction and learning from the refinement of artificial intelligence which is easier to industrialize. Through the interdisciplinary integration and optimization construction of the curriculum, this course system is suitable for universities. According to developed course content, design the teaching schedule, and then, based on this scheme, we will cultivate edge computing research and application-oriented talents, and support the transformation and upgrading of industrial automation to intelligent.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127081832","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}
To break the data barrier of the information island and explore the value of data in the past few years, it has become a trend of uploading data to the cloud by data owners for data sharing. At the same time, they also hope that the uploaded data can still be controlled, which makes access control of cloud data become an intractable problem. As a famous cryptographic technology, ciphertext policy-based attribute encryption (CP-ABE) not only assures data confidentiality but implements fine-grained access control. However, the actual application of CP-ABE has its inherent challenge in attribute revocation. To address this challenge, we proposed an access control solution supporting attribute revocation in cloud computing. Unlike previous attribute revocation schemes, to solve the problem of excessive attribute revocation overhead, we use symmetric encryption technology to encrypt the plaintext data firstly, and then, encrypting the symmetric key by utilizing public-key encryption technology according to the access structure, so that only the key ciphertext is necessary to update when the attributes are revoked, which reduces the spending of ciphertext update to a great degree. The comparative analysis demonstrates that our solution is reasonably efficient and more secure to support attribute revocation and access control after data sharing.
{"title":"Access Control Scheme Supporting Attribute Revocation in Cloud Computing","authors":"Yachen He, Guishan Dong, Dong Liu, Haiyang Peng, Yuxiang Chen","doi":"10.1109/NaNA53684.2021.00072","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00072","url":null,"abstract":"To break the data barrier of the information island and explore the value of data in the past few years, it has become a trend of uploading data to the cloud by data owners for data sharing. At the same time, they also hope that the uploaded data can still be controlled, which makes access control of cloud data become an intractable problem. As a famous cryptographic technology, ciphertext policy-based attribute encryption (CP-ABE) not only assures data confidentiality but implements fine-grained access control. However, the actual application of CP-ABE has its inherent challenge in attribute revocation. To address this challenge, we proposed an access control solution supporting attribute revocation in cloud computing. Unlike previous attribute revocation schemes, to solve the problem of excessive attribute revocation overhead, we use symmetric encryption technology to encrypt the plaintext data firstly, and then, encrypting the symmetric key by utilizing public-key encryption technology according to the access structure, so that only the key ciphertext is necessary to update when the attributes are revoked, which reduces the spending of ciphertext update to a great degree. The comparative analysis demonstrates that our solution is reasonably efficient and more secure to support attribute revocation and access control after data sharing.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131850670","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00088
Cuicui Niu, Zhengzhi Pan, Wuchao Shi, Hai Liu
Network construction is beneficial to extract global structural information from vector data. However, node-link information is easy to bring the problem of privacy leakage in network construction. Thus, we provided node-link preserving in network construction via overlapping histogram under local differential privacy. First, we present the general model of network construction using overlapping histogram under local differential privacy. Second, we present algorithm of random perturbation-linking in network construction via overlapping histogram to preserve node-link. Finally, our theoretical and experimental results show that the proposed scheme can achieve the tradeoff between node-link protection and network utility. The proposed scheme provides a basic method against node-link leakage in network construction via overlapping histogram.
{"title":"Network Construction Using Overlapping Histogram under Local Differential Privacy","authors":"Cuicui Niu, Zhengzhi Pan, Wuchao Shi, Hai Liu","doi":"10.1109/NaNA53684.2021.00088","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00088","url":null,"abstract":"Network construction is beneficial to extract global structural information from vector data. However, node-link information is easy to bring the problem of privacy leakage in network construction. Thus, we provided node-link preserving in network construction via overlapping histogram under local differential privacy. First, we present the general model of network construction using overlapping histogram under local differential privacy. Second, we present algorithm of random perturbation-linking in network construction via overlapping histogram to preserve node-link. Finally, our theoretical and experimental results show that the proposed scheme can achieve the tradeoff between node-link protection and network utility. The proposed scheme provides a basic method against node-link leakage in network construction via overlapping histogram.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124887374","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00034
Yichuan Wang, He Wang, Xinhong Hei, Wenjiang Ji, Lei Zhu
In recent years, a variety of network attacks emerge in an endless stream, and network attacks gradually show the characteristics of higher secrecy and greater harm. At present, the analysis of system vulnerabilities is generally focused on the characteristics analysis and impact hazard level, and lack of formal modeling and vulnerability analysis methods. In this paper, we model the OpenSSL service’s Heartbleed vulnerability based on Petri net. We build a formal model combined with the source code and system state, analyze the vulnerability of the system running state, and propose an automatic vulnerability repair scheme. Experiments show that this model can carry out fine-grained formal analysis and simulation of the Heartbleed, which is of great significance to explore the system vulnerability modeling method.
{"title":"Petri net modeling and vulnerability analysis of the Heartbleed","authors":"Yichuan Wang, He Wang, Xinhong Hei, Wenjiang Ji, Lei Zhu","doi":"10.1109/NaNA53684.2021.00034","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00034","url":null,"abstract":"In recent years, a variety of network attacks emerge in an endless stream, and network attacks gradually show the characteristics of higher secrecy and greater harm. At present, the analysis of system vulnerabilities is generally focused on the characteristics analysis and impact hazard level, and lack of formal modeling and vulnerability analysis methods. In this paper, we model the OpenSSL service’s Heartbleed vulnerability based on Petri net. We build a formal model combined with the source code and system state, analyze the vulnerability of the system running state, and propose an automatic vulnerability repair scheme. Experiments show that this model can carry out fine-grained formal analysis and simulation of the Heartbleed, which is of great significance to explore the system vulnerability modeling method.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996748","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00065
Zhang Lin
In many practical applications, due to the high cost of data annotation, the training dataset includes a large number of unlabeled samples and a small number of labeled samples. At the same time, there are a large number of normal behavior data and a small number of intrusion data in the network data. In order to solve this problem, this paper proposes a semi-supervised ensemble learning algorithm for imbalanced data. This algorithm uses the relationship between class samples to define the sampling probability of samples, and then constructs the initial training subset and the base classifier according to the sampling probability. Then, the evaluation index for imbalanced data is defined to evaluate and select base classifiers. Then the weighted voting method is used to integrate the selected base classifier. Finally, the simulation results of UCI data set and NSL-KDD data set show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.
{"title":"Network Intrusion Detection based of Semi-Supervised Ensemble Learning Algorithm for Imbalanced Data","authors":"Zhang Lin","doi":"10.1109/NaNA53684.2021.00065","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00065","url":null,"abstract":"In many practical applications, due to the high cost of data annotation, the training dataset includes a large number of unlabeled samples and a small number of labeled samples. At the same time, there are a large number of normal behavior data and a small number of intrusion data in the network data. In order to solve this problem, this paper proposes a semi-supervised ensemble learning algorithm for imbalanced data. This algorithm uses the relationship between class samples to define the sampling probability of samples, and then constructs the initial training subset and the base classifier according to the sampling probability. Then, the evaluation index for imbalanced data is defined to evaluate and select base classifiers. Then the weighted voting method is used to integrate the selected base classifier. Finally, the simulation results of UCI data set and NSL-KDD data set show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125310268","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00062
Junpeng Zhang, Mengqian Li, Shuiguang Zeng, B. Xie, Dongmei Zhao
Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.
{"title":"A survey on security and privacy threats to federated learning","authors":"Junpeng Zhang, Mengqian Li, Shuiguang Zeng, B. Xie, Dongmei Zhao","doi":"10.1109/NaNA53684.2021.00062","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00062","url":null,"abstract":"Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124567535","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00035
Guiling Zhang, Yaling Zhang, Yichuan Wang, Lei Zhu, Wenjiang Ji
With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security.
{"title":"A fine-grained petri model for SQL time-blind injection","authors":"Guiling Zhang, Yaling Zhang, Yichuan Wang, Lei Zhu, Wenjiang Ji","doi":"10.1109/NaNA53684.2021.00035","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00035","url":null,"abstract":"With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020103","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 : 2021-10-01DOI: 10.1109/nana53684.2021.00005
{"title":"Message from the General Conference Chairs","authors":"","doi":"10.1109/nana53684.2021.00005","DOIUrl":"https://doi.org/10.1109/nana53684.2021.00005","url":null,"abstract":"","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116150998","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00012
Wentao Du, Xinyv Ma, Wenxiang Dong, Dong Zhang, Chi Zhang, Qibin Sun
Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred increasing interest nowadays. A promising approach for privacy-preserving graph neural networks is to apply local differential privacy (LDP). Though LDP provides protection against privacy attacks, the calibration of the privacy budget is not well understood and the relationship between privacy protection level and model utility is not well established. In this paper, we propose an evaluation method to characterize the trade-off between utility and privacy for locally private graph neural networks (LPGNNs). More specifically, we leverage the effect of attribute inference attacks as a privacy measurement to bridge the gaps among the model utility, privacy leakage, and the value of the privacy budget. Our experimental results show that the LPGNNs model may fulfill the promise of providing privacy protection against powerful opponents by providing poor model utility, and when it provides acceptable utility, it shows moderate vulnerability to the attribute inference attacks. Moreover, one of the direct applications of our method is visualizing the adjusting of privacy budgets and facilitating the deployment of LDP.
{"title":"Calibrating Privacy Budgets for Locally Private Graph Neural Networks","authors":"Wentao Du, Xinyv Ma, Wenxiang Dong, Dong Zhang, Chi Zhang, Qibin Sun","doi":"10.1109/NaNA53684.2021.00012","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00012","url":null,"abstract":"Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred increasing interest nowadays. A promising approach for privacy-preserving graph neural networks is to apply local differential privacy (LDP). Though LDP provides protection against privacy attacks, the calibration of the privacy budget is not well understood and the relationship between privacy protection level and model utility is not well established. In this paper, we propose an evaluation method to characterize the trade-off between utility and privacy for locally private graph neural networks (LPGNNs). More specifically, we leverage the effect of attribute inference attacks as a privacy measurement to bridge the gaps among the model utility, privacy leakage, and the value of the privacy budget. Our experimental results show that the LPGNNs model may fulfill the promise of providing privacy protection against powerful opponents by providing poor model utility, and when it provides acceptable utility, it shows moderate vulnerability to the attribute inference attacks. Moreover, one of the direct applications of our method is visualizing the adjusting of privacy budgets and facilitating the deployment of LDP.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122615468","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00083
Yaling Zhang, Hongtao Wang, Yichuan Wang, Wenjiang Ji, Lei Zhu
Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%.
{"title":"Signature Scheme Based on The SM2 Algorithm in Fabric","authors":"Yaling Zhang, Hongtao Wang, Yichuan Wang, Wenjiang Ji, Lei Zhu","doi":"10.1109/NaNA53684.2021.00083","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00083","url":null,"abstract":"Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125656105","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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