MS
Data & AnalyticsFull-Stack Developer

Data Anonymization Tool

A privacy-first web app that anonymizes sensitive datasets while retaining analytical value.

PythonStreamlitPandasData Privacy Techniques
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Problem & Challenge

The Objective

Organizations needed a way to share sensitive datasets without compromising privacy or research usability.

Approach & Architecture

The Process

  • 1
    Identified sensitive fields and anonymization requirements
  • 2
    Implemented masking, pseudonymization, and noise injection techniques
  • 3
    Built a clean UI for one-click CSV uploads
  • 4
    Tested usability with sample datasets
  • 5
    Deployed on Streamlit for easy access by non-technical users
Key Impact & Results

“Enabled secure data sharing and compliance with privacy regulations for non-technical users.”