Aisha Zahid Junejo, Manzoor Ahmed Hashmani, Abdullah Abdulrehman Alabdulatif
{"title":"通过在交易广播期间限制验证节点来保护区块链隐私","authors":"Aisha Zahid Junejo, Manzoor Ahmed Hashmani, Abdullah Abdulrehman Alabdulatif","doi":"10.1109/ICECCE52056.2021.9514212","DOIUrl":null,"url":null,"abstract":"The increasing awareness of the Blockchain technology has gained interest of researchers and industrialists in recent years, hence several business enterprises are keen on using blockchain technology for their day-to-day transactions and record keeping. However, due to public availability of data on the blockchain, it is not advisable for organizations dealing with sensitive and confidential data to risk their data privacy by using blockchain networks. In this study we talk about privacy vulnerabilities and challenges in blockchain based applications. We verify the extent of the problem by both, literary findings, and empirical analyses. Next, we propose a conceptual framework to strengthen privacy preservation of the blockchain networks. The proposed framework is based on the idea of limiting the number of nodes that a transaction is broadcast to, for verification. The selected nodes will differ for each transaction, decreasing the possibilities of network listening and deanonymization of users. Moreover, limiting the number of verifying nodes will result in drastic reduction of computation overhead of the network, along with improved scalability. The proposed framework is analyzed based on various privacy features and risks. The evaluation results show that the model has a privacy rank of 0.76.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Blockchain Privacy Preservation by Limiting Verifying Nodes' During Transaction Broadcasting\",\"authors\":\"Aisha Zahid Junejo, Manzoor Ahmed Hashmani, Abdullah Abdulrehman Alabdulatif\",\"doi\":\"10.1109/ICECCE52056.2021.9514212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing awareness of the Blockchain technology has gained interest of researchers and industrialists in recent years, hence several business enterprises are keen on using blockchain technology for their day-to-day transactions and record keeping. However, due to public availability of data on the blockchain, it is not advisable for organizations dealing with sensitive and confidential data to risk their data privacy by using blockchain networks. In this study we talk about privacy vulnerabilities and challenges in blockchain based applications. We verify the extent of the problem by both, literary findings, and empirical analyses. Next, we propose a conceptual framework to strengthen privacy preservation of the blockchain networks. The proposed framework is based on the idea of limiting the number of nodes that a transaction is broadcast to, for verification. The selected nodes will differ for each transaction, decreasing the possibilities of network listening and deanonymization of users. Moreover, limiting the number of verifying nodes will result in drastic reduction of computation overhead of the network, along with improved scalability. The proposed framework is analyzed based on various privacy features and risks. The evaluation results show that the model has a privacy rank of 0.76.\",\"PeriodicalId\":302947,\"journal\":{\"name\":\"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECCE52056.2021.9514212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECCE52056.2021.9514212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Blockchain Privacy Preservation by Limiting Verifying Nodes' During Transaction Broadcasting
The increasing awareness of the Blockchain technology has gained interest of researchers and industrialists in recent years, hence several business enterprises are keen on using blockchain technology for their day-to-day transactions and record keeping. However, due to public availability of data on the blockchain, it is not advisable for organizations dealing with sensitive and confidential data to risk their data privacy by using blockchain networks. In this study we talk about privacy vulnerabilities and challenges in blockchain based applications. We verify the extent of the problem by both, literary findings, and empirical analyses. Next, we propose a conceptual framework to strengthen privacy preservation of the blockchain networks. The proposed framework is based on the idea of limiting the number of nodes that a transaction is broadcast to, for verification. The selected nodes will differ for each transaction, decreasing the possibilities of network listening and deanonymization of users. Moreover, limiting the number of verifying nodes will result in drastic reduction of computation overhead of the network, along with improved scalability. The proposed framework is analyzed based on various privacy features and risks. The evaluation results show that the model has a privacy rank of 0.76.