{"title":"Blockchain Electoral Vote Counting Solutions: A Comparative Analysis of Methods, Constraints, and Approaches","authors":"Patrick Mwansa, Boniface Kabaso","doi":"10.1109/icABCD59051.2023.10220467","DOIUrl":null,"url":null,"abstract":"Blockchain technology in electronic voting has emerged as an alternative to other electronic and paper-based voting systems to minimize inconsistencies and redundancies. However, past experiences indicate limited success due to scalability, speed, and privacy issues. This systematic literature review examines the methods, constraints, and approaches in the existing literature on blockchain-based electoral vote-counting solutions. A thorough search of pertinent databases was performed, and selected studies were assessed based on predefined inclusion and exclusion criteria. The review's findings reveal that most existing solutions employ smart contracts and various cryptographic algorithms to create secure and transparent voting systems. However, the study also pinpoints areas that require improvement, such as scalability, privacy, and accessibility. The review recommends exploring different combinations of blockchain platforms, cryptographic algorithms, and programming languages to develop secure and transparent voting systems. Additionally, future research could investigate the potential benefits and challenges of incorporating Internet of Things (IoT) devices, consensus mechanisms, and other technologies into the voting process. The review concludes that more research is needed to enhance the security and transparency of blockchain-based voting systems.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"1 1","pages":"1-10"},"PeriodicalIF":2.6000,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/icABCD59051.2023.10220467","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Blockchain technology in electronic voting has emerged as an alternative to other electronic and paper-based voting systems to minimize inconsistencies and redundancies. However, past experiences indicate limited success due to scalability, speed, and privacy issues. This systematic literature review examines the methods, constraints, and approaches in the existing literature on blockchain-based electoral vote-counting solutions. A thorough search of pertinent databases was performed, and selected studies were assessed based on predefined inclusion and exclusion criteria. The review's findings reveal that most existing solutions employ smart contracts and various cryptographic algorithms to create secure and transparent voting systems. However, the study also pinpoints areas that require improvement, such as scalability, privacy, and accessibility. The review recommends exploring different combinations of blockchain platforms, cryptographic algorithms, and programming languages to develop secure and transparent voting systems. Additionally, future research could investigate the potential benefits and challenges of incorporating Internet of Things (IoT) devices, consensus mechanisms, and other technologies into the voting process. The review concludes that more research is needed to enhance the security and transparency of blockchain-based voting systems.
Big DataCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍:
Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
Big Data coverage includes:
Big data industry standards,
New technologies being developed specifically for big data,
Data acquisition, cleaning, distribution, and best practices,
Data protection, privacy, and policy,
Business interests from research to product,
The changing role of business intelligence,
Visualization and design principles of big data infrastructures,
Physical interfaces and robotics,
Social networking advantages for Facebook, Twitter, Amazon, Google, etc,
Opportunities around big data and how companies can harness it to their advantage.