Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152768
Yahui Lu, Yuping Jiang
The memory dirty page prediction technology can effectively predict whether a memory page will be modified (dirty) at the next moment, and is widely used in virtual machine migration, container migration and other fields. In this paper, we propose a machine-learning based method for memory dirty page prediction. The method exploits the temporal and spatial locality principle of memory changes, collects dirty records of pages over a period of time, and uses supervised learning methods for training and predicting the dirty page. We also discuss the influence of data contradiction and data repetition in memory page dataset. The experiments with different memory change frequency dataset show that compared with the traditional time series methods, our machine-learning based method has better performance.
{"title":"Dirty page prediction by machine learning methods based on temporal and spatial locality","authors":"Yahui Lu, Yuping Jiang","doi":"10.1109/CSCWD57460.2023.10152768","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152768","url":null,"abstract":"The memory dirty page prediction technology can effectively predict whether a memory page will be modified (dirty) at the next moment, and is widely used in virtual machine migration, container migration and other fields. In this paper, we propose a machine-learning based method for memory dirty page prediction. The method exploits the temporal and spatial locality principle of memory changes, collects dirty records of pages over a period of time, and uses supervised learning methods for training and predicting the dirty page. We also discuss the influence of data contradiction and data repetition in memory page dataset. The experiments with different memory change frequency dataset show that compared with the traditional time series methods, our machine-learning based method has better performance.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"21 1","pages":"89-94"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73906136","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152568
Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu
Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.
{"title":"Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition","authors":"Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu","doi":"10.1109/CSCWD57460.2023.10152568","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152568","url":null,"abstract":"Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"57 1","pages":"843-848"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77407709","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152696
Xi Song, Mingyang Li, Weidong Xie, Yuanyuan Mao
This paper investigates a traveling salesman problem (TSP), which has important applications in real-world scenarios. A reinforcement learning-driven iterated greedy algorithm (RLIGA) is presented to address the TSP. A population initialization method based on the famous FRB2 heuristic is proposed to generate an initial population with high quality. To enhance the effectiveness of the RLIGA, the local search method and the destruction-construction mechanisms are designed for the city sequence. A generation method of sub-population based on current population sequence information is proposed to generate sub-population. An acceptance criterion is proposed to determine whether the offspring are adopted into the population. A re-destruction and re-construction method is proposed to avoid the proposed algorithm falling into local optimum. Lastly, the RLIGA is tested on the TSPLIB benchmark instances. The experimental results show that RLIGA is an effective algorithm to address the problem.
{"title":"A Reinforcement Learning-driven Iterated Greedy Algorithm for Traveling Salesman Problem","authors":"Xi Song, Mingyang Li, Weidong Xie, Yuanyuan Mao","doi":"10.1109/CSCWD57460.2023.10152696","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152696","url":null,"abstract":"This paper investigates a traveling salesman problem (TSP), which has important applications in real-world scenarios. A reinforcement learning-driven iterated greedy algorithm (RLIGA) is presented to address the TSP. A population initialization method based on the famous FRB2 heuristic is proposed to generate an initial population with high quality. To enhance the effectiveness of the RLIGA, the local search method and the destruction-construction mechanisms are designed for the city sequence. A generation method of sub-population based on current population sequence information is proposed to generate sub-population. An acceptance criterion is proposed to determine whether the offspring are adopted into the population. A re-destruction and re-construction method is proposed to avoid the proposed algorithm falling into local optimum. Lastly, the RLIGA is tested on the TSPLIB benchmark instances. The experimental results show that RLIGA is an effective algorithm to address the problem.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1342-1347"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77226511","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152707
António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca
Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.
{"title":"Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk","authors":"António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca","doi":"10.1109/CSCWD57460.2023.10152707","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152707","url":null,"abstract":"Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"26 3","pages":"77-82"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72394065","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152715
Xu Sun, Weiyu Zhang, Xinchao Guo, Wenpeng Lu
Community detection is an important task in graph analysis, and it is of great significance in reality. Recently, unsupervised learning has been widely used in community detection tasks. However, only a few community detection models combine unsupervised learning with graph neural networks (GNNs). To this end, in this paper, we combine GNNs with unsupervised learning to propose a new model, Unsupervised graph neural network with Self-expressive attention for Community detection (USCom). We first use the graph attention encoder to generate node embeddings. Then we apply the self-expressive principle to optimize the node embeddings to make them more suitable for community detection tasks. Finally, we utilize a four-layer perceptron for community detection. The experimental results show that the model proposed in this paper outperforms the comparison baselines on community detection tasks.
{"title":"Unsupervised Graph Neural Network with Self-Expressive Attention for Community Detection","authors":"Xu Sun, Weiyu Zhang, Xinchao Guo, Wenpeng Lu","doi":"10.1109/CSCWD57460.2023.10152715","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152715","url":null,"abstract":"Community detection is an important task in graph analysis, and it is of great significance in reality. Recently, unsupervised learning has been widely used in community detection tasks. However, only a few community detection models combine unsupervised learning with graph neural networks (GNNs). To this end, in this paper, we combine GNNs with unsupervised learning to propose a new model, Unsupervised graph neural network with Self-expressive attention for Community detection (USCom). We first use the graph attention encoder to generate node embeddings. Then we apply the self-expressive principle to optimize the node embeddings to make them more suitable for community detection tasks. Finally, we utilize a four-layer perceptron for community detection. The experimental results show that the model proposed in this paper outperforms the comparison baselines on community detection tasks.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"31 1","pages":"1890-1895"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85233561","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}
Traditional malicious code detection methods require a lot of manpower and resources, which makes the research of malicious code very difficult. The selection of malicious code features mainly relies on the subjective analysis and selection of experts, which has a large impact on the detection effect of the model. In this paper, malicious codes are converted into greyscale images as model inputs, and features are automatically extracted using a deep-learning model. An improved convolutional neural network model based on Xception (Simplified Xception) is proposed for malicious code family classification. The model reduces the number of modules in the original model and adds a depth-separable convolutional layer with a step size of 2 to enhance the generated grey-scale images. The model is compared with CNN models, ResNet50, and improved models related to Inception. The experimental results show that the accuracy of SimplifiedXception is 98%, which is better than other related models. Compared to the Xception model, the accuracy of the Simplified-Xception model was improved by 1.3% and the number of parameters was reduced by half.
{"title":"Simplified-Xception: A New Way to Speed Up Malicious Code Classification","authors":"Xinshuai Zhu, Songheng He, Xuren Wang, Chang Gao, Yushi Wang, Peian Yang, Yuxia Fu","doi":"10.1109/CSCWD57460.2023.10152755","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152755","url":null,"abstract":"Traditional malicious code detection methods require a lot of manpower and resources, which makes the research of malicious code very difficult. The selection of malicious code features mainly relies on the subjective analysis and selection of experts, which has a large impact on the detection effect of the model. In this paper, malicious codes are converted into greyscale images as model inputs, and features are automatically extracted using a deep-learning model. An improved convolutional neural network model based on Xception (Simplified Xception) is proposed for malicious code family classification. The model reduces the number of modules in the original model and adds a depth-separable convolutional layer with a step size of 2 to enhance the generated grey-scale images. The model is compared with CNN models, ResNet50, and improved models related to Inception. The experimental results show that the accuracy of SimplifiedXception is 98%, which is better than other related models. Compared to the Xception model, the accuracy of the Simplified-Xception model was improved by 1.3% and the number of parameters was reduced by half.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 1","pages":"582-587"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85627107","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}
Aiming at the problems of privacy disclosure and false information provided by malicious vehicles in vehicular ad-hoc network (VANET) communication, this paper proposes a group signature authentication scheme with credit evaluation mechanism combining certificateless public key cryptography and group signature technology. In this scheme, the problem of certificate management and key escrow were solved by using certificateless public key cryptography; secondly, used group signature technology, any member of the group could sign the message anonymously on behalf of the group to protect the private information of the vehicle; finally, a single factor weight evaluation mechanism and a reward and punishment mechanism were introduced to evaluate the reliability of shared information and encourage users to share real information. Based on the computational Diffie-Hellman difficult problem, the scheme is proved to satisfy the security of signature unforgeability under the random oracle model. Compared with the existing schemes, the experimental results show that the scheme reduces the time of vehicle signature by 10.82%~45.95%, and the time of group administrator verification by 4.87%~30.09%, which proves that the scheme is more effective.
{"title":"Group Signature Authentication Scheme with Credit Evaluation Mechanism in VANET","authors":"Yanfei Lu, Suzhen Cao, Qizhi He, Zixuan Fang, Junjian Yan, Yi Guo","doi":"10.1109/CSCWD57460.2023.10152641","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152641","url":null,"abstract":"Aiming at the problems of privacy disclosure and false information provided by malicious vehicles in vehicular ad-hoc network (VANET) communication, this paper proposes a group signature authentication scheme with credit evaluation mechanism combining certificateless public key cryptography and group signature technology. In this scheme, the problem of certificate management and key escrow were solved by using certificateless public key cryptography; secondly, used group signature technology, any member of the group could sign the message anonymously on behalf of the group to protect the private information of the vehicle; finally, a single factor weight evaluation mechanism and a reward and punishment mechanism were introduced to evaluate the reliability of shared information and encourage users to share real information. Based on the computational Diffie-Hellman difficult problem, the scheme is proved to satisfy the security of signature unforgeability under the random oracle model. Compared with the existing schemes, the experimental results show that the scheme reduces the time of vehicle signature by 10.82%~45.95%, and the time of group administrator verification by 4.87%~30.09%, which proves that the scheme is more effective.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"34 1","pages":"1703-1709"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85082052","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152701
Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou
Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.
{"title":"A Constraint-based Recommender System via RDF Knowledge Graphs","authors":"Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou","doi":"10.1109/CSCWD57460.2023.10152701","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152701","url":null,"abstract":"Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"849-854"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86085578","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152826
Yueyao Chen, Beilun Wang, Tianyi Ma, Cheng Chen
Nowadays, federated learning has been a prevailing paradigm for large-scale distributed machine learning, which is faced with the problem of communication bottleneck. To solve this problem, recent works usually apply different compression techniques such as sparsification and quantization compressors. However, such approaches are all lossy compression and have two drawbacks. First, they could lead to information loss of the global parameter. Second, compressed parameters carrying less information would be more likely to be attacked by malicious workers than full parameters, leading to a Byzantine failure of the model. In this paper, to avoid information loss, mitigate the communication bottleneck, and at the same time tolerate popular Byzantine attacks, we propose FedGraD, which leverages gradient difference compression and combines robust aggregation rules in federated learning settings. Our experimental results on three different datasets a9a, w8a and mushrooms show good performance of our method.
{"title":"Applying Robust Gradient Difference Compression to Federated Learning","authors":"Yueyao Chen, Beilun Wang, Tianyi Ma, Cheng Chen","doi":"10.1109/CSCWD57460.2023.10152826","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152826","url":null,"abstract":"Nowadays, federated learning has been a prevailing paradigm for large-scale distributed machine learning, which is faced with the problem of communication bottleneck. To solve this problem, recent works usually apply different compression techniques such as sparsification and quantization compressors. However, such approaches are all lossy compression and have two drawbacks. First, they could lead to information loss of the global parameter. Second, compressed parameters carrying less information would be more likely to be attacked by malicious workers than full parameters, leading to a Byzantine failure of the model. In this paper, to avoid information loss, mitigate the communication bottleneck, and at the same time tolerate popular Byzantine attacks, we propose FedGraD, which leverages gradient difference compression and combines robust aggregation rules in federated learning settings. Our experimental results on three different datasets a9a, w8a and mushrooms show good performance of our method.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"3 1","pages":"1748-1753"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72461426","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 : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152733
Qianqian Chang, Lin Xu, L. Zhang
The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.
{"title":"Practical privacy-preserving mixing protocol for Bitcoin","authors":"Qianqian Chang, Lin Xu, L. Zhang","doi":"10.1109/CSCWD57460.2023.10152733","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152733","url":null,"abstract":"The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 6","pages":"17-22"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72470666","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}
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