A Stratified Data Security Model Based On Data Priority Using The Advanced Encryption Standard (Aes) And An Enhanced Diffie-Hellman Algorithm

The rate of infringements and modifications by cybercriminals to computer documents and data connected to the Internet is immeasurable in our contemporary time. One common form of security breach involves embedding spyware or malicious viruses that secretly monitor user activities and transmit sensitive documents to hackers. Such threats pose serious challenges for computer users and can have devastating effects on individuals, businesses, governments, and other institutions. This study seeks to develop a stratified data security framework that prioritizes data according to its importance, employing the Advanced Encryption Standard (AES) and the Diffie-Hellman algorithm to strengthen existing data protection measures. The system was implemented using Javascript with MySQL at the back end. The testing was done using hypothetical banking transaction details which are classified into three main priority levels: most sensitive, medium sensitive and least sensitive and was test-run on a local server. The results showed that the AES combined with the enhanced Diffie-Hellman can be used to encrypt and protect organizational data based on their importance and can deter unauthorized access. It is recommended that the system be adopted and put into practice to strengthen data integrity and safeguard against unauthorized access to personal files as well as networked computer data across different levels of priority.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-27
DOI
https://doi.org/10.5281/zenodo.17101337
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Stratified Data Security Model Based On Data Priority Using The Advanced Encryption Standard (Aes) And An Enhanced Diffie-Hellman Algorithm

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

A Stratified Data Security Model Based On Data Priority Using The Advanced Encryption Standard (Aes) And An Enhanced Diffie-Hellman Algorithm

article en

Abstract

The rate of infringements and modifications by cybercriminals to computer documents and data connected to the Internet is immeasurable in our contemporary time. One common form of security breach involves embedding spyware or malicious viruses that secretly monitor user activities and transmit sensitive documents to hackers. Such threats pose serious challenges for computer users and can have devastating effects on individuals, businesses, governments, and other institutions. This study seeks to develop a stratified data security framework that prioritizes data according to its importance, employing the Advanced Encryption Standard (AES) and the Diffie-Hellman algorithm to strengthen existing data protection measures. The system was implemented using Javascript with MySQL at the back end. The testing was done using hypothetical banking transaction details which are classified into three main priority levels: most sensitive, medium sensitive and least sensitive and was test-run on a local server. The results showed that the AES combined with the enhanced Diffie-Hellman can be used to encrypt and protect organizational data based on their importance and can deter unauthorized access. It is recommended that the system be adopted and put into practice to strengthen data integrity and safeguard against unauthorized access to personal files as well as networked computer data across different levels of priority.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 46%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.