Blockchain Based Secure voting System

A simplified model to improve electoral integrity through Blockchain

technology in Oman’s digital voting systems. As digital voting systems gain attention on

security and transparency so ensuring the integrity of the process through robust security. This

research proposed a robust model based on Blockchain, with its decentralized, immutable,

and transparent nature, which offers a promising solution to these challenges. The proposed

model uses Blockchain technology to create a secure and trustworthy voting environment,

addressing key vulnerabilities in traditional and digital electoral processes. The architecture

of the proposed simplified Blockchain-based distributed voting system comprises four tiers,

collectively enhancing transparency, security, and reliability in electoral operations. Findings

indicate that Blockchain can significantly improve electoral integrity by providing a tamper-

proof record of voters, candidates, and election results, facilitating real-time auditability, and

ensuring secure voter authentication. The research concludes that Blockchain technology holds

substantial promise in revolutionizing digital voting in Oman, paving the way for more secure

and trustworthy election outcomes. This research provides valuable insights to policymakers for

them to consider Blockchain technology as an alternative for electoral reforms, emphasizing the

need for pilot programs and collaborative efforts to refine and implement this innovative solution

effectively.

Synthetic Data Generation Method:

The enclosed dataset contains citizens' demographic information, including National IDs and associated attributes. The data was created using a Python-based synthetic data generation script, which aims to simulate real-world demographic datasets while ensuring privacy and confidentiality. The dataset served as a useful tool for testing, training, and validating proposed model titled “Blockchain enhanced electoral integrity: a robust model for secure digital voting systems in Oman”

The dataset is designed to simulate real-world demographic data while preserving privacy and confidentiality, offering a safe and controlled environment for model development, performance evaluation, and experimentation. Since the data is synthetic, it does not represent real individuals, and no personal or sensitive data was collected or used in the creation of the dataset. However, it adheres to the common structures and characteristics typically found in demographic datasets, ensuring its utility for research and experimentation without compromising privacy.

Here is the python code:

"from faker import Faker

import random

import csv

fake = Faker()

def generate_synthetic_data(num_rows):

data = []

for _ in range(num_rows):

name = fake.first_name()

surname = fake.last_name()

date_of_birth = fake.date_of_birth(minimum_age=18, maximum_age=90)

residential_address = fake.street_address()

mailing_address = fake.street_address() if random.choice([True, False]) else None

citizenship_status = 'Citizen'

national_id = fake.random_int(min=100000000, max=999999999)

contact_email = fake.email()

contact_phone = fake.phone_number()

signature = f"{name[0]} {surname[0]}"

data.append([name, surname, date_of_birth, residential_address, mailing_address,

citizenship_status, national_id, contact_email, contact_phone, signature])

return data

def save_to_csv(data, filename):

with open(filename, 'w', newline='') as file:

writer = csv.writer(file)

writer.writerow(["Name", "Surname", "Date of Birth", "Residential Address",

"Mailing Address", "Citizenship Status", "National ID",

"Contact Email", "Contact Phone", "Signature"])

writer.writerows(data)

if __name__ == "__main__":

num_rows = 1000

synthetic_data = generate_synthetic_data(num_rows)

save_to_csv(synthetic_data, 'synthetic_data_with_national_id.csv')"