Pub Date : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274082
Mujistapha Ahmed Safana, Y. Arafa, Jixin Ma
Blockchain technology has proven to be secured and reliable technology by bringing security, trust and data integrity to distributed systems. It brought a new paradigm that helps in the existence of the cryptocurrency and eliminating the third party in a financial transaction. It has the potential of optimising, enhancing streamlining many processes outside the cryptocurrency and financial sector but the adoption of the technology is limited by the hindering performance issues. These issues are mostly around the Proof-of-Work (PoW) consensus protocol that is used by Bitcoin and Ethereum and referred to as the most secured and decentralised protocol thus, the most reliable. Unfortunately, the protocol suffers a performance degrade with the increasing size and number of transactions because of its complexity. Many industries, researchers and organisation have been working on addressing these issues but most of the attempts result in facing another issue referred to as the scalability issue; having to trade off one of security or decentralisation to get speed. Other solution such as Bitcoin lighting network improved the transaction throughput of the Bitcoin without technically addressing the issue on the blockchain, therefore, didn’t face the scalability issue. This paper presents a research work of a novel approach that propose using machine learning techniques to improve the performance by enhancing the mining efficiency of the protocol. It uses the Ethereum network as a case study. The paper also aims to compare the predictive accuracy and speed of some machine learning regression models against the traditional mining method starting from the Linear regression. The objective is to determine whether using machine learning in the mining process offers a faster way of achieving consensus, therefore improving performance by reducing the time and energy consumption of the protocol without sacrificing security or decentralisation. The proposed model results in improved accuracy and faster consensus from the early experiments.
{"title":"Improving the performance of the Proof-of-Work Consensus Protocol Using Machine learning","authors":"Mujistapha Ahmed Safana, Y. Arafa, Jixin Ma","doi":"10.1109/BCCA50787.2020.9274082","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274082","url":null,"abstract":"Blockchain technology has proven to be secured and reliable technology by bringing security, trust and data integrity to distributed systems. It brought a new paradigm that helps in the existence of the cryptocurrency and eliminating the third party in a financial transaction. It has the potential of optimising, enhancing streamlining many processes outside the cryptocurrency and financial sector but the adoption of the technology is limited by the hindering performance issues. These issues are mostly around the Proof-of-Work (PoW) consensus protocol that is used by Bitcoin and Ethereum and referred to as the most secured and decentralised protocol thus, the most reliable. Unfortunately, the protocol suffers a performance degrade with the increasing size and number of transactions because of its complexity. Many industries, researchers and organisation have been working on addressing these issues but most of the attempts result in facing another issue referred to as the scalability issue; having to trade off one of security or decentralisation to get speed. Other solution such as Bitcoin lighting network improved the transaction throughput of the Bitcoin without technically addressing the issue on the blockchain, therefore, didn’t face the scalability issue. This paper presents a research work of a novel approach that propose using machine learning techniques to improve the performance by enhancing the mining efficiency of the protocol. It uses the Ethereum network as a case study. The paper also aims to compare the predictive accuracy and speed of some machine learning regression models against the traditional mining method starting from the Linear regression. The objective is to determine whether using machine learning in the mining process offers a faster way of achieving consensus, therefore improving performance by reducing the time and energy consumption of the protocol without sacrificing security or decentralisation. The proposed model results in improved accuracy and faster consensus from the early experiments.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116868107","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 : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274464
Seoyoung Ko, Xinxin Fan, Zhi-Yao Zhong, Qi Chai
Staking is an essential component in Proof-of-Stake (PoS) based blockchain systems. While a host of PoS blockchains have staking schemes in place, the implementations of those mechanisms are highly customized to meet the needs of specific blockchains and vary in terms of the offered functionalities. In this paper, we present EMS, an extensible and modular staking architecture for PoS systems. EMS specifies a generic and modular staking implementation framework by applying a novel bucket-based data structure across different system components. In particular, EMS is able to accommodate a variety of design requirements for staking in PoS systems by manipulating the optional fields in the bucket-based data structure, thereby providing great flexibility and extensibility. Our instantiation of EMS on the IoTeX blockchain further demonstrates its viability and effectiveness in practice.
{"title":"EMS: An Extensible and Modular Staking Architecture for Proof-of-Stake Systems","authors":"Seoyoung Ko, Xinxin Fan, Zhi-Yao Zhong, Qi Chai","doi":"10.1109/BCCA50787.2020.9274464","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274464","url":null,"abstract":"Staking is an essential component in Proof-of-Stake (PoS) based blockchain systems. While a host of PoS blockchains have staking schemes in place, the implementations of those mechanisms are highly customized to meet the needs of specific blockchains and vary in terms of the offered functionalities. In this paper, we present EMS, an extensible and modular staking architecture for PoS systems. EMS specifies a generic and modular staking implementation framework by applying a novel bucket-based data structure across different system components. In particular, EMS is able to accommodate a variety of design requirements for staking in PoS systems by manipulating the optional fields in the bucket-based data structure, thereby providing great flexibility and extensibility. Our instantiation of EMS on the IoTeX blockchain further demonstrates its viability and effectiveness in practice.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125527194","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 : 2020-11-02DOI: 10.1109/bcca50787.2020.9274080
{"title":"BCCA 2020 Preface","authors":"","doi":"10.1109/bcca50787.2020.9274080","DOIUrl":"https://doi.org/10.1109/bcca50787.2020.9274080","url":null,"abstract":"","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839029","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 : 2020-11-02DOI: 10.1109/bcca50787.2020.9274457
{"title":"BCCA 2020 TOC","authors":"","doi":"10.1109/bcca50787.2020.9274457","DOIUrl":"https://doi.org/10.1109/bcca50787.2020.9274457","url":null,"abstract":"","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125078527","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 : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274081
Baran Kiliç, C. Özturan, A. Sen
Ability to perform fast analysis on massive public blockchain transaction data is needed in various finance applications such as tracing of fraudulent activities. The blockchain data that is synced as a node is accessible as a sequence of blocks containing transactions. This way of accessing transaction data, however, is too slow for applications that require a transaction graph to be constructed. We develop a cluster based system that constructs a distributed transaction graph in parallel. Since blockchain data is continuously growing, our parallel system also offers the advantage of being able to scale by simply increasing the number of nodes in the cluster. Our system has been developed using the MPI message passing interface. We report performance results from our system operating on the whole 9.5 million block (roughly 4 year) Ethereum mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution and token transaction counting on a 16 node economical cluster set up on the Amazon cloud. In particular, our system is able to construct distributed graph of 658 million ether and 31 major token transactions in 188 seconds.
{"title":"A Cluster Based System for Analyzing Ethereum Blockchain Transaction Data","authors":"Baran Kiliç, C. Özturan, A. Sen","doi":"10.1109/BCCA50787.2020.9274081","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274081","url":null,"abstract":"Ability to perform fast analysis on massive public blockchain transaction data is needed in various finance applications such as tracing of fraudulent activities. The blockchain data that is synced as a node is accessible as a sequence of blocks containing transactions. This way of accessing transaction data, however, is too slow for applications that require a transaction graph to be constructed. We develop a cluster based system that constructs a distributed transaction graph in parallel. Since blockchain data is continuously growing, our parallel system also offers the advantage of being able to scale by simply increasing the number of nodes in the cluster. Our system has been developed using the MPI message passing interface. We report performance results from our system operating on the whole 9.5 million block (roughly 4 year) Ethereum mainnet blockchain data. We report timings obtained from tests involving distributed transaction graph construction, partitioning, page ranking of addresses, degree distribution and token transaction counting on a 16 node economical cluster set up on the Amazon cloud. In particular, our system is able to construct distributed graph of 658 million ether and 31 major token transactions in 188 seconds.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130628934","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 : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274462
S. Thakur, J. Breslin
Blockchain offline channels such as the Bitcoin Lightning Network will improve the scalability of blockchains by reducing the number of transactions needed to be recorded in the blockchain. Uncoordinated payment transfer in channel networks including path-based fund transfer may overuse few channels. This will lead to imbalanced channel networks where the channel balance of few channels is too low to remain operational. In this paper, we propose a coordination method for a landmark-based routing algorithm for fund transfer in offline channels. Our procedure allows the landmarks to route funds in complementary and non-overlapping paths which balances channel values in a bi-directional channel network. Using experimental evaluation with Bitcoin Lightning network data we prove that the proposed coordinated landmark-based routing algorithm keeps a better balance of the channels and significantly improves the success rate of fund transfer compared with existing landmark-based routing algorithms.
{"title":"Coordinated Landmark-based Routing for Blockchain Offline Channels","authors":"S. Thakur, J. Breslin","doi":"10.1109/BCCA50787.2020.9274462","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274462","url":null,"abstract":"Blockchain offline channels such as the Bitcoin Lightning Network will improve the scalability of blockchains by reducing the number of transactions needed to be recorded in the blockchain. Uncoordinated payment transfer in channel networks including path-based fund transfer may overuse few channels. This will lead to imbalanced channel networks where the channel balance of few channels is too low to remain operational. In this paper, we propose a coordination method for a landmark-based routing algorithm for fund transfer in offline channels. Our procedure allows the landmarks to route funds in complementary and non-overlapping paths which balances channel values in a bi-directional channel network. Using experimental evaluation with Bitcoin Lightning network data we prove that the proposed coordinated landmark-based routing algorithm keeps a better balance of the channels and significantly improves the success rate of fund transfer compared with existing landmark-based routing algorithms.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019841","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 : 2020-11-02DOI: 10.1109/bcca50787.2020.9274448
{"title":"BCCA 2020 List Reviewer Page","authors":"","doi":"10.1109/bcca50787.2020.9274448","DOIUrl":"https://doi.org/10.1109/bcca50787.2020.9274448","url":null,"abstract":"","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129465539","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 : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274454
K. Martin, I. Alsmadi, M. Rahouti, M. Ayyash
Blockchain is an emerging technology that enables a vital framework for various cryptocurrency operations such as bitcoin. Notably, without any involvement from third party authorities, blockchain offers a decentralized consensus scheme to process user transactions, fund transfer, and various data records in a secure and reliable way. Furthermore, bitcoin price forecasting has been a vital research trend, where machine learning techniques play a substantial role. A sophisticated and appropriately trained model can be useless if the features being tested are unreliable. Independently, one of the most desirable aspects of a system that utilizes the blockchain is the concrete objectiveness by which each entry is cataloged. Any data collected and reported on the blockchain is unambiguous, and therefore, extremely suitable for a machine learning algorithm. To efficiently forecast bitcoin price movements, in this work, we propose and examine various lenses by which to view this union, each with varying degrees of success.
{"title":"Combining Blockchain and Machine Learning to Forecast Cryptocurrency Prices","authors":"K. Martin, I. Alsmadi, M. Rahouti, M. Ayyash","doi":"10.1109/BCCA50787.2020.9274454","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274454","url":null,"abstract":"Blockchain is an emerging technology that enables a vital framework for various cryptocurrency operations such as bitcoin. Notably, without any involvement from third party authorities, blockchain offers a decentralized consensus scheme to process user transactions, fund transfer, and various data records in a secure and reliable way. Furthermore, bitcoin price forecasting has been a vital research trend, where machine learning techniques play a substantial role. A sophisticated and appropriately trained model can be useless if the features being tested are unreliable. Independently, one of the most desirable aspects of a system that utilizes the blockchain is the concrete objectiveness by which each entry is cataloged. Any data collected and reported on the blockchain is unambiguous, and therefore, extremely suitable for a machine learning algorithm. To efficiently forecast bitcoin price movements, in this work, we propose and examine various lenses by which to view this union, each with varying degrees of success.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134532967","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 : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274463
Kazumasa Omote, Asuka Suzuki, Teppei Sato
Trust of user address (i.e., wallet address) in Bitcoin is dependent on not who user is but the amount of Bitcoin currency in user’s wallet address. So we cannot necessarily trust a Bitcoin user with wallet address. Actually, there is no method for assigning trust to user addresses in Bitcoin. Although we can provide trust to user address by inserting certificate into Bitcoin transaction using OP_RETURN, it still has some problems about wastefulness. In this paper, we propose a new method that efficiently assigns trust to user address using Bitcoin transfer only, which does not need to use OP_RETURN. Our proposed method can realize a trust providing mechanism for “Blockchain 1.0” without adding any new function. Main idea of our method is that “token coin” (Bitcoin transfer itself) can be used as a trust providing mechanism. For feasibility study, we experimentally show the trust assigning in the Bitcoin testnet. Furthermore, our method can be easily adopted into other cryptocurrencies.
{"title":"A New Method of Assigning Trust to User Addresses in Bitcoin","authors":"Kazumasa Omote, Asuka Suzuki, Teppei Sato","doi":"10.1109/BCCA50787.2020.9274463","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274463","url":null,"abstract":"Trust of user address (i.e., wallet address) in Bitcoin is dependent on not who user is but the amount of Bitcoin currency in user’s wallet address. So we cannot necessarily trust a Bitcoin user with wallet address. Actually, there is no method for assigning trust to user addresses in Bitcoin. Although we can provide trust to user address by inserting certificate into Bitcoin transaction using OP_RETURN, it still has some problems about wastefulness. In this paper, we propose a new method that efficiently assigns trust to user address using Bitcoin transfer only, which does not need to use OP_RETURN. Our proposed method can realize a trust providing mechanism for “Blockchain 1.0” without adding any new function. Main idea of our method is that “token coin” (Bitcoin transfer itself) can be used as a trust providing mechanism. For feasibility study, we experimentally show the trust assigning in the Bitcoin testnet. Furthermore, our method can be easily adopted into other cryptocurrencies.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127047344","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 : 2020-11-02DOI: 10.1109/BCCA50787.2020.9274460
Panayiotis Christodoulou, Klitos Christodoulou
Continuous efforts are being made daily by organizations to develop special tools that can support the collection and organization of available information in order to prevent the spread of fake news. Nowadays, misinformation has been deemed as a great challenge especially with the raise of social media networks that are used as platforms for disseminating information in a digital form. This paper presents the implementation of a decentralized application deployed on the Ethereum blockchain, to be used as a tool for combating fake news and misinformation. The develop framework is proposed as a solution for publishing reliable news sources and enabling readers to self-verify the trustworthiness of the published source. An experimental scenario has been implemented that presents the effectiveness of the proposed framework.
{"title":"Developing more Reliable News Sources by utilizing the Blockchain technology to combat Fake News","authors":"Panayiotis Christodoulou, Klitos Christodoulou","doi":"10.1109/BCCA50787.2020.9274460","DOIUrl":"https://doi.org/10.1109/BCCA50787.2020.9274460","url":null,"abstract":"Continuous efforts are being made daily by organizations to develop special tools that can support the collection and organization of available information in order to prevent the spread of fake news. Nowadays, misinformation has been deemed as a great challenge especially with the raise of social media networks that are used as platforms for disseminating information in a digital form. This paper presents the implementation of a decentralized application deployed on the Ethereum blockchain, to be used as a tool for combating fake news and misinformation. The develop framework is proposed as a solution for publishing reliable news sources and enabling readers to self-verify the trustworthiness of the published source. An experimental scenario has been implemented that presents the effectiveness of the proposed framework.","PeriodicalId":218474,"journal":{"name":"2020 Second International Conference on Blockchain Computing and Applications (BCCA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134174591","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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