Asian Journal of Computer Science and Technology (AJCST)
A Novel Digital Voting System with Integrated Technologies (DVSIT)Author : T. M. N. Vamsi, S. Sri Charan Dutta, K. Harika, V. Kiran and V. Abhiram
Volume 8 No.2 April-June 2019 pp 19-24
Over the years, technology has been growing fast. With growing technology there are many modern problems being created. With innovation many novel approaches are being proposed to find a better solution. Coming to the present days, the “Electronic Voting Machines (“EVM”)” based voting system is prone to much vulnerability. There were several issues regarding tampering and security of EVMs which have not been proved. With the upcoming elections the biggest challenge is to conduct a fraud-free polling. Due to vulnerabilities in current voting system the security of a vote is being compromised by many malpractices such as duplicate voters, dummy candidates, booth capturing, EVM rigging, etc. Introducing Blockchain Technology into digital voting process can minimize most of the frauds as it’s almost impossible to breach the security level of Blockchain. The proposed system assures authenticity of a voter by providing Dual Authentication process.
EVM, Security, Fraud-Free Polling, Malpractices, Blockchain, Authenticity, Dual Authentication Process
 P. Fredrik, Hjálmarsson and Gunnlaugur K. Hreiðarsson, “Block chain-Based E-Voting System”, IEEE 11th International Conference on Cloud Computing (CLOUD), pp.983-986, 2018.
 VarunGarg and KritikaGarg, “Face Recognition Using Haar Cascade Classifier”, Journal of Emerging Technologies and Innovative Research (JETIR), Vol. 3, No. 12, December 2016.
 Ali KaanKoç, EmreYavuz, Umut Can Çabuk and GökhanDalkiliç, “Towards Secure E-Voting Using EthereumBlockchain”, 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1-6, 2018.
 Aftab Ahmed, JiandongGuo, Fayaz Ali and Farah deeba, “LBPH Based Improved Face Recognition At Low Resolution”, in International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 144-146, 2018.
 J. W. L. Chao, J. J. Ding, J. Z. Liu,“Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection”, Elsevier-Signal Processing, Vol. 117, pp. 1-10, 2015.
 J. B. A. Olshausan and D. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images”, Nature Publishing, Vol. 381, No. 6583, pp. 607-609, 1996.