Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Streaming Media
Event Website
https://databasemasking.carrd.co/#
Document Type
Event
Start Date
15-4-2025 4:00 PM
Description
The Database Masking Tool for Gwinnett County Public Schools secures sensitive data while preserving its analytical utility. Developed alongside an in-depth research paper, this web-based solution enables real-time masking of information in SQL Server and MySQL databases. Utilizing automated field recognition, it applies three masking techniques—Faker-based masking, hash masking, and pseudonymization through generalized masking—to protect personally identifiable information. Key features include an intuitive interface for configuring masking rules, real-time data previews, and an export function for generating masked datasets in multiple formats. Built with a React-Flask stack and containerized for consistency, the system supports compliance with GDPR, HIPAA, and FERPA. Guided by feedback from sponsor Ed Van Ness and academic advisors, this project establishes a robust framework for scalable data anonymization, enhancing operational efficiency and regulatory compliance.
UC-125 Database Masking Tool - Project 04 - Team 1
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
The Database Masking Tool for Gwinnett County Public Schools secures sensitive data while preserving its analytical utility. Developed alongside an in-depth research paper, this web-based solution enables real-time masking of information in SQL Server and MySQL databases. Utilizing automated field recognition, it applies three masking techniques—Faker-based masking, hash masking, and pseudonymization through generalized masking—to protect personally identifiable information. Key features include an intuitive interface for configuring masking rules, real-time data previews, and an export function for generating masked datasets in multiple formats. Built with a React-Flask stack and containerized for consistency, the system supports compliance with GDPR, HIPAA, and FERPA. Guided by feedback from sponsor Ed Van Ness and academic advisors, this project establishes a robust framework for scalable data anonymization, enhancing operational efficiency and regulatory compliance.
https://digitalcommons.kennesaw.edu/cday/Spring_2025/Undergraduate_Project/25
